curiculum vitae - IIT Demokritos

Transcription

CURICULUM VITAE
CONSTANTINE D. SPYROPOULOS
ECCAI FELLOW
M.Sc., Ph.D. in Computer Science
Research Director
Institute of Informatics & Telecommunications
N.C.S.R. “DEMOKRITOS”
Athens, June 2010
1
APPENDIX I: CITATIONS DATA
Α1. NON-SELF CITED CITATIONS
Γημοζιεύζεις ζε Γιεθνή Δπιζηημονικά Περιοδικά
G Sigletos, G Paliouras, CD Spyropoulos, M Hatzopoulos ―Combining Information
Extraction Systems Using Voting and Stacked Generalization‖, The Journal of
Machine Learning Research archive, Volume 6, December 2005 (cited by 20)
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Wong Tak-Lam, Lam Wai. Learning to adapt web information extraction knowledge and
discovering new attributes via a bayesian approach. IEEE Transactions on Knowledge
and Data Engineering, vol. 22, no. 4, pp 523-536, 2010
Michael Zuckermana, Ariel D. Procacciab and Jeffrey S. Rosenschein ―Algorithms for
the coalitional manipulation problem‖, in Artificial Intelligence, Volume 173, Issue 2,
February 2009, Pages 392-412
Z Chen, DV Kalashnikov, S Mehrotra ―Exploiting context analysis for combining
multiple entity resolution systems‖, in Proceedings of the 35th SIGMOD international
conference on Management of data, pp 207-218, 2009
Dah-Jye Leea, Sameer Antanib, Yuchou Changa, Kent Gledhillc, L. Rodney Longb and
Paul Christensen ―CBIR of spine X-ray images on inter-vertebral disc space and shape
profiles using feature ranking and voting consensus‖, in Data & Knowledge Engineering,
Volume 68, Issue 12, December 2009, Pages 1359-1369
David Leake and Joseph Kendall-Morwick ―Four Heads Are Better than One: Combining
Suggestions for Case Adaptation‖, in Case-Based Reasoning Research and Development,
Volume 5650/2009, pp 165-179
Tak-Lam Wonga and Wai Lam ―An unsupervised method for joint information extraction
and feature mining across different Web sites‖, Data & Knowledge Engineering Volume
68, Issue 1, January 2009, Pages 107-125
Andreas Janecek ―Efficient Feature Reduction and Classification Methods‖, Dissertation,
University of Vienna, 2009
Prasanna Sridhar ―Scalability and Performance Issues in Deeply Embedded Sensor
Systems‖, in International Journal On Smart Sensing And Intelligent Systems, Vol. 2, No.
1, March 2009
M Banko, O Etzioni, T Center ―The tradeoffs between open and traditional relation
extraction‖, Proceedings of the 46th Annual Meeting of the …, 2008
Y Chang, S Antani, DJ Lee, K Gledhill ―CBIR of spine X-ray images on inter-vertebral
disc space and shape profiles‖, 21st IEEE International Symposium on Computer- …,
2008
DJ Russomanno, M Yeasin, E Jacobs ―Sparse detector sensor: profiling experiments for
broad-scale classification‖, Proc. SPIE 2008
AD Procaccia ―Computational Voting Theory: Of the Agents, By the Agents, For the
Agents‖, thesis 2008
G Tepvorachai ―An Evolutionary Platform for Retargetable Image and Signal Processing
Applications‖, thesis 2008
Alberto Lavelli,Mary Elaine Califf,Fabio Ciravegna, Dayne Freitag, Claudio Giuliano,
Nicholas Kushmerick, Lorenza Romano and Neil Ireson ―Evaluation of machine learningbased information extraction algorithms: criticisms and recommendations‖, in Language
Resources and Evaluation, Volume 42, Number 4 / December, 2008
Y Chang, DJ Lee, Y Hong, J Archibald ―Edge Detection from Global and Local Views
Using an Ensemble of Multiple Edge Detectors‖, Proceedings of the 4th International
Symposium on …, 2008
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16. FA Barros, EFA Silva, RBC Prudêncio ―Hidden Markov Models and Text Classifiers for
Information Extraction on Semi-Structured Texts‖, Proceedings of the 2008 8th
International Conference …, 2008
17. Peleg, B., Procaccia, A.: Mediators and truthful voting. Working paper (2008)
18. M. Banko, M. J. Cafarella, S. Soderland, M. Broadhead, and O. Etzioni. Open
information extraction from the Web. In Proceedings of the 20th International Joint
Conference on Artificial Intelligence, 2007
19. Talib, Fatima, ―Computational aspects of voting: a literature survey‖, in RIT Digital
Library, 2007
20. Ioannis Caragiannis and Ariel D. Procaccia, ―Low-Distortion Embeddings into Voting
Rules‖, 2007 (http://www.cs.huji.ac.il/~arielpro/papers/embedding.pdf)
G. Petasis, G. Paliouras, V. Karkaletsis, C. Halatsis, and C.D. Spyropoulos, ―eGRIDS: Computationally Efficient Grammatical Inference from Positive
Examples‖. GRAMMARS, (7) 2004, pp. 69-110, (cited by 11)
21. C de la Higuera, ‗Grammatical inference: learning automata and grammars‘, Cambridge
Univarsity Press, 2010
22. R Reichart, A Rappoport ―Unsupervised induction of labeled parse trees by clustering
with syntactic features‖, in Proceedings of the 22nd International Conference on
Computational Linguistics - Volume 1, pp 721-8, 2008
23. W Zuidema ―An annotated bibliography of grammar induction models for natural
language learning‖, paper at Institute for Logic, Language and Computation, University
of Amsterdam, May 23, 2008
24. R. Eyraud, C. de la Higuera and J-C Janodet, ―LARS: A Learning Algorithm for
Rewriting Systems‖, Machine Learning Journal , 2007, vol.66, No1, pp.7-31.
25. G. Borensztajn, and W. Zuidema. 2007. Bayesian Model Merging for Unsupervised
Constituent Labeling and Grammar Induction. Technical Report. ILLC.
26. MATSUNO, I.P. Um Estudo do Processo de Inferência de Gramáticas Regulares e Livres
de Contexto Baseados em Modelos Adaptativos. Dissertação de Mestrado, USP, São
Paulo, 2006
27. Alpana Dubey, ―Inferring Grammar Rules from Programs‖, PhD Thesis, Indian Institute
Of Technology Kanpur, 2006
28. Rémi Eyraud, ―Inférence grammaticale de langages hors-contextes‖, PhD thesis,
University of Saint-Etienne, 2006
29. C. de la Higuera, & J. Oncina (2006). Learning context-free languages. Artificial
Intelligence Reviews.
30. Ivone Penque Matsuno, ―Um Estudo dos Processos de Inferencia de Gramaticas
Regulares e Livres de Contexto Baseados em Modelos Adaptivos‖ Master Dissertation,
2006.
31. R. Eyraud, C. de la Higuera and J-C Janodet, ―Representing Languages by Learnable
Rewriting Systems‖, Proceedings of the 7th International Colloquium on Grammatical
Inference (ICGI), Lecture Notes in Computer Science, 3264, pp. 139- 150, 2004.
D. Pierrakos, G. Paliouras, C. Papatheodorou and C.D. Spyropoulos, ―Web
Usage Mining as a tool for personalization: a survey,‖ User Modeling and UserAdapted Interaction, 13(4), pp. 311-372, November 2003. (cited by 119)
32. Knud Möller, Michael Hausenblas, Richard Cyganiak, Siegfried Handschuh, Gunnar
Grimnes "Learning from Linked Open Data Usage: Patterns & Metrics", Web Science
Conference 2010, 2010
33. Karin Beckera, Corresponding Author Contact Information, E-mail The Corresponding
Author and Mariângela Vanzin, ―O3R: Ontology-based mechanism for a human-centered
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environment targeted at the analysis of navigation patterns‖, in Knowledge-Based
Systems, Volume 23, Issue 5, July 2010, Pages 455-470
P.Venketesh, R Venkatesan, L. Arunprakash, ―Semantic Web Prefetching Scheme Using
Naïve Bayes Classifier‖, in International Journal of Computer Science and Applications,
Vol. 7, No. 1, pp. 66 – 78, 2010
Markus Zanker and Markus Jessenitschnig, ―Case-studies on exploiting explicit customer
requirements in recommender systems‖, in User Modeling and User-Adapted Interaction,
Volume 19, Numbers 1-2, pp 133-166, 2009
Marti A. Hearst, ―Search User Interfaces‖, 1 st Version, Book, Pages: 408, Cambridge
University Press, Year of Publication: 2009
凌海峰，刘业政，杨善林, “基于蚁群算法与 K-means 算法相结合的 Web
用户聚类”, in 情报学报, v. 4, pp. 105-108, 2009
Thanassis Hadzilacos, Dimitris Kalles, Dionysis Karaiskakis, Maria Pouliopoulou,
―Using Graphs in Developing Educational Material‖, in Proceedings of the 2nd
International Workshop on Building Technology Enhanced Learning solutions for
Communities of Practice, pp 55-66, 2009
Mrs Geeta R.B., Prof. Shashikumar G.Totad, Dr. Prasad Reddy PVGD, ―Amalgamation
of Web Usage Mining and Web Structure Mining‖, in International Journal of Recent
Trends in Engineering, Vol. 1, No. 2, May 2009
I-Hsien Ting, Lillian Clark, Chris Kimble, ―Identifying web navigation behaviour and
patterns automatically from clickstream data‖, in International Journal of Web
Engineering and Technology, Volume 5, Number 4, pp 398 - 426 , 2009
Giovanna Castellano, Anna Maria Fanelli, Maria Alessandra Torsello and Lakhmi C.
Jain, ―Innovations in Web Personalization‖, in Web Personalization in Intelligent
Environments, Volume 229/2009, pp 1-26
Matthias Mayer, ―Web History Tools and Revisitation Support: A Survey of Existing
Approaches and Directions‖, book 2009
Hamid Rastegari, Siti Mariyam Shamsuddin, "Review of Artificial Immune System in
Web Personalization," socpar, pp.699-702, 2009 International Conference of Soft
Computing and Pattern Recognition, 2009
I-Cheng Yeha, Che-hui Lienb, Tao-Ming Tingc and Chin-Hao Liu, ―Applications of web
mining for marketing of online bookstores‖, in Expert Systems with Applications,
Volume 36, Issue 8, October 2009, Pages 11249-11256
I-Hsien Ting, Hui-Ju Wu, Pei-Shan Chang, ―Analyzing Multi-source Social Data for
Extracting and Mining Social Networks‖, in Proceedings of the 2009 International
Conference on Computational Science and Engineering, Volume 04, pp 815-820, 2009
H Deng, ―Web Mining Techniques for Query Log Analysis and Expertise Retrieval‖,
PhD Thesis, The Chinese University of Hong Kong, 2009
Luis Carlos Couto de Souza, ―Metodologia De Mineração De Dados Aplicada A
Navegação De Dispositivos Móveis‖, Dissertation, 2009
张炜，洪霞, ―基于 OPAC 读者行为挖掘的个性化服务系统关键技术分析‖, v3,
pp62-64, 2009
Fadoua Ouamani, Hajer Baazaoui Zghal, Zeina Jrad, Marie-Aude Aufaure, Henda Ben
Ghézala, ―Conception d‘un système multi-agent du Web Usage Mining pour la
personnalisation du web‖ in MUPIW-EGC08 page 47, 2008.
MF Rutledge-Taylor, A Vellino, RL West, ―A Holographic Associative Memory
Recommender System‖, Digital Information Management, 2008. ICDIM 2008.
G Castellano, AM Fanelli, MA Torsello ―Computational Intelligence techniques for Web
personalization‖, Web Intelligence and Agent Systems, 2008 IOS Press
M Zanker, M Jessenitschnig ―Case-studies on exploiting explicit customer requirements
in recommender systems‖, User Modeling and User-Adapted Interaction Journal,
Volume 19, Numbers 1-2, pp 133-166, 2008
M Hadjouni, H Baazaoui, MA Aufaure, C Claramunt ―Towards a personalized spatial
web architecture‖, in Workshop ―Semantic Web meets Geospatial Applications‖, held in
conjunction with AGILE 2008, 11th International Conference on Geographic Information
Science, on Monday, May 5th, 2008.
M Rupert, A Rattrout, S Hassas, ―The web from a complex adaptive systems
perspective‖, Journal of Computer and System Sciences, Volume 74, Issue 2, March
2008, Pages 133-145.
4
55. D Zhu, H Dreher, WA Perth ―Improving Web Search by Categorization, Clustering, and
Personalization‖, In ADMA '08, pages 659{666, Berlin, Heidelberg, 2008
56. Montgomery, Alan and Michael Smith (2008), ―Prospects for Personalization on the
Internet,‖ Journal of Interactive Marketing, 23 (2), 130–7
57. G Castellano, AM Fanelli, P Plantamura ―A Neuro-Fuzzy Strategy for Web
Personalization‖, in aaai 2008
58. T Hadzilacos, D Kalles, D Karaiskakis, M Pouliopoulou ―Using Graphs in Developing
Educational Material‖, in ―Web-Based Learning Solutions for Communities of Practice:
Developing Virtual Enviroments for Social and Pedagogical Advancement‖ by Nikos
Karacapilidis, premier reference source, 2008, Chapter 5 pp 55-61
59. Juan D. Velásquez, Vasile Palade, Adaptive Web Sites - A Knowledge Extraction from
Web Data Approach, ISBN 978-1-58603-831-1, IOS Press, 2008
60. DN Sotiropoulos, AS Lampropoulos, GA Tsihrintzis ―Individualization of Content-Based
Image Retrieval Systems via Objective Feature Subset Selection‖, in Multimedia Services
in Intelligent Environments, Volume 120/2008 pp 181-201
61. Q Zhang, Rs Segall ―Web Mining: A Survey Of Current Research, Techniques, And
Software‖, - in International Journal of Information Technology & Decision Making
(IJITDM) , Volume: 7, Issue: 4 (2008) pp. 683-720
62. G Korfiatis ―Modeling Web Navigation Using Grammatical Inference‖ in Applied
Artificial Intelligence (AAI 2008), 2008
63. Le Bigot, L., Bretier, P., Terrier, P.: Detecting and exploiting user familiarity in natural
language human-computer dialogue. In: Asai, K. (ed.) Human Computer Interaction: New
Developments, pp. 269–382. InTech Education and Publishing (2008); ISBN: 978-9537619-14-5
64. MA Bayir, IH Toroslu, A Cosar, G Fidan ―Discovering More Accurate Frequent Web
Usage Patterns‖, Arxiv preprint arXiv:0804.1409, 2008
65. Hollink, V., Van Someren, M., and De Boer, V. (2008). Capturing the needs of amateur
web designers by means of examples. In Proceedings of the 16th Workshop on Adaptivity
and User Modeling in Interactive Systems, Würzburg, Germany, pages 26–31
66. A Cuzzocrea ―Knowledge Personalization in Web-Services-Based Environments: A
Unified Approach‖, in Evolution of the Web in Artificial Intelligence Environments,
Volume 130/2008 pp 101-135. 2008
67. B Mojtaba, I Mashhad, M Reza ―A New Hybrid Recommender System Using Dynamic
Fuzzy Clustering‖, http://profsite.um.ac.ir/~rmonsefi/conferences/baghebani1.pdf, 2008
68. S Park, NC Suresh, BK Jeong, ―Sequence-based clustering for Web usage mining: A new
experimental framework and ANN-enhanced K-means algorithm‖, in Data & Knowledge
Engineering, Volume 65, Issue 3, June 2008, Pages 512-543
69. Cristina Gena and Stephan Weibelzahl, ―Usability Engineering for the Adaptive Web‖, In
Lecture Notes in Computer Science, vol. 4321, pp. 720-762, 2007.
70. Susanne Gudrun Burklen, ―Vorabübertragung schwach strukturierter Informationen in
ortsbasierten mobilen Systemen‖, PhD thesis, 2007.
71. Pythagoras Karampiperis and Aristeidis Diplaros, ―Exploiting Image Segmentation
Techniques for Social Filtering of Educational Content‖, 2007
72. Chen Ding and Jin Zhou, ―Multiple Evidence Combination in Web Site Search Based on
Users‘ Access Histories‖,
Lecture Notes in Computer Science, Book: ―User
Modeling 2007‖, Vol. 4511, pp. 405-409, 2007.
73. K Markellos, P Markellou, A Panayiotaki, A Tsakalidis, ―Semantic Web Mining for
Personalized Public E-Services‖, in Global E-government: Theory, Applications and
Benchmarking, pp.1-21, 2007.
74. 宁小红，余森森, ―Study on s-Tree Algorithm for Personalized Recommendation‖,
2007.
75. Anna Goy, Liliana Ardissono and Giovanna Petrone, ―Personalization in E-Commerce
Applications‖, Lecture Notes in Computer Science, ―The Adaptive Web‖, Vol. 4321,
pp.485-520, 2007.
76. Rosario Girardi , Leandro Balby Marinho, A domain model of Web recommender
systems based on usage mining and collaborative filtering, Requirements Engineering,
v.12 n.1, p.23-40, 2007.
77. G. Castellano, A. M. Fanelli, M. A. Torsello, ―Log Data Preparation For Mining Web
Usage Patterns‖, IADIS International Conference Applied Computing 2007, pp.371-378,
2007.
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78. J Vesanen, ―What is personalization? A conceptual framework‖, European Journal of
Marketing Vol. 41 No. 5/6, 2007, pp. 409-418
79. M Barla, M Bielikova, M. Barla and M. Bielikova ―Estimation of User Characteristics
using Rule-based Analysis of User Logs‖. In Data Mining for User Modeling Proceedings
of Workshop held at the International Conference on User Modeling UM2007, pages
5{14, Corfu, Greece, 2007.
80. T Hadzilacos, D Kalles, M Pouliopoulou, ―On the Software and Knowledge Engineering
Aspects of the Educational Process‖, in International Journal of Software Engineering and
Knowledge Engineering (IJSEKE), 2007.
81. V Hollink, M van Someren, BJ Wielinga, ―Navigation behavior models for link structure
optimization‖, in User Modeling and User-Adapted Interaction, Vol 17, No 4, pp.339377, 2007.
82. DN Sotiropoulos, AS Lampropoulos, GA Tsihrintzis, ―MUSIPER: a system for modeling
music similarity perception based on objective feature subset selection‖, in User
Modeling and User-Adapted Interaction, 2007.
83. G Castellano, F Mesto, M Minunno, MA Torsello, ―Web User Profiling Using Fuzzy
Clustering‖, in Lecture Notes in Computer Science, book: Applications of Fuzzy Sets
Theory, Vol 4578, pp.94-101, 2007
84. S Mongy, F Bouali, C Djeraba, ―Analyzing User‘s Behavior on a Video Database‖,
SpringerMultimedia Data Mining and Knowledge Discovery, Part V, pp. 458-471,
2007.
85. M. Virvou, A. Savvopoulos, G. A. Tsihrintzis and D. N. Sotiropoulos, ―Constructing
Stereotypes for an Adaptive e-Shop Using AIN-Based Clustering‖, Springer- Lecture
Notes in Computer Science, book: Adaptive and Natural Computing Algorithms, Volume
4431, pp. 837-845, 2007.
86. David Albrecht and Ingrid Zukerman, ―Introduction to the special issue on statistical and
probabilistic methods for user modeling‖, Springer- User Modeling and User-Adapted
Interaction, Volume 17, Numbers 1-2 / March, 2007, pp. 1-4.
87. T Dalamagas, P Bouros, T Galanis, M Eirinaki and T Sellis, ―Mining user navigation
patterns for personalizing topic directories‖, Proceedings of the 9th annual ACM
―international workshop on Web information and data management‖, Lisbon, Portugal,
pp.81-88, 2007.
88. H Lam, D Russell, D Tang, T Munzner, ―Session Viewer: Visual Exploratory Analysis of
Web Session Logs‖, IEEE Symposium on Visual Analytics Science and Technology,
Sacramento, CA, USA, 2007.
89. Hollink, M., van Someren, M., Wielinga, B. ―Discovering stages in web navigation for
problemoriented navigation support‖ User Model. User Adapt. Interact., vol.17, No 1-2,
March, 2007, pp 183-214.
90. Herder, H. (2006). Forward, back, and home again: Analyzing user behavior on the Web.
Ph.D. Thesis, University of Twente.
91. Stefani, B. Vassiliadis and M. Xenos, ―Behavioural patterns in hypermedia systems: a
study of e-commerce vs. e-learning practices‖, Proceedings of the International Workshop
on Adaptive and Personalized Semantic Web, 16th ACM Conference on Hypertext and
Hypermedia (HT), Salzburg, Austria, 2006.
92. Xi HuiDan;Yan Hui, ―The Application of Concept Lattice in Web_log Mining‖,
Computer Systems& Applications, 2006.
93. XU Jing, CAI Qiong,YU Jun-jie, ―Application and Research of Web Log Mining Based
on Fuzzy Clustering‖, Computer Knowledge and Technology(Academic Exchange), 2006
94. Mostafa Hanoune*, Fouzia Benabbou, ―Traitement et exploration du fichier Log du
Serveur Web, pour l‘extraction des connaissances: Web Usage Mining‖, 2006.
95. Yanwu Yang, ―L‘ Ecole Nationale Supérieure D‘Arts et Métiers‖, Towards Spatial Web
Personalization, PhD Thesis, 2006
96. G. Castellano, A. M. Fanelli, M. A. Torsello, ―Dynamic Link Suggestion By A NeuroFuzzy Web Recommendation System‖, IADIS International Conference WWW/Internet
2006.
97. M Bober, P Šaloun, ―Adaptation of Navigation by the Modified Results of Full Scan
Algorithm in Adaptive Hypermedia Systems‖, 2006
98. JD Velasquez, V Palade, ―Testing Online Navigation Recommendations in a Web Site‖,
in Lecture Notes in Computer Science, Knowledge-Based Intelligent Information and
Engineering Systems, vol. 4253, pp.487-496, 2006.
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99. Q Song, M Shepperd, ―Mining web browsing patterns for E-commerce‖, Computers in
Industry, Vol 57, Issue 7, September 2006, Pages 622-630, 2006
100. C Schwendtner, F König, A Paramythis, ―Prospector: An adaptive front-end to the
Google search engine‖, LWA 2006
101. T. Maier, ―Modeling ETL for Web Usage Analysis and Further Improvements of the Web
Usage Analysis Process‖, PhD Thesis, 2006.
102. Carlos Soares, Edgar de Graaf, Joost N. Kok, ―Sequence Mining On Web Access Logs: A
Case Study‖, 2006.
103. B Zhou, SC Hui, ACM Fong, ―An Effective Approach for Periodic Web Personalization‖,
Proceedings of the 2006 IEEE/WIC/ACM International Conference on Web Intelligence,
pp 284-292, 2006.
104. Prasanna Desikan and Jaideep Srivastava, ―Mining Temporally Changing Web Usage
Graphs‖, In Lecture Notes in Computer Science, book: Advances in Web Mining and
Web Usage Analysis, vol. 3932, pp.1-17, 2006.
105. Alfredo Cuzzocrea, ―Combining multidimensional user models and knowledge
representation and management techniques for making web services knowledge-aware‖.
In ―Web Intelligence and Agent Systems‖, Issue: Volume 4, Number 3, pp.289-312,
2006.
106. M.Rigou, S. Sirmakessis, G. Tzimas, ―A method for Personalized Clustering in Data
Intensive Web Applications‖, Conference on Hypertext and Hypermedia-Proceedings of
the joint international workshop on Adaptivity, personalization & the semantic web,
Odense, Denmark p.p. 35-40 , 2006.
107. Sriram Kalyanaraman & S. Shyam Sundar, ―The Psychological Appeal of Personalized
Content in Web Portals: Does Customization Affect Attitudes and Behavior?‖ Journal of
Communication, Volume 56 Issue 1 Page 110 - March 2006.
108. Przemysław Kazienko, Marcin Pilarczyk, ―Hyperlink assessment based on web usage
mining”, Conference on Hypertext and Hypermedia -Proceedings of the seventeenth
conference on Hypertext and hypermedia, Odense, Denmark, Pages: 85 - 88, 2006.
109. R. Kaschek , C. Matthews , K.-D. Schewe and C. Wallace, ―Information systems design:
through adaptivity to ubiquity‖, Information Systems and E-Business Management, vol.
4, n. 2, pp. 137-158, April 2006.
110. Z. Zeng and G. Yao, ―System for personalization based on web mining‖, Computer
Engineering and Design, vol. 27, no. 7, pp. 1155-1157, 2006.
111. M. Rupert, S. Hassas and A. Rattrout, ―The Web and Complex Adaptive Systems‖,
Proceedings of the 20th International Conference on Advanced Information Networking
and Applications (AINA), pp. 200-204, Vienna, Austria, 2006.
112. E. Michopoulou and D. Buhalis, ―Developing an eTourism Platform for Accessible
Tourism in Europe: Technical Challenges‖, Proceedings of the 13th International
Conference on Information Technology and Travel & Tourism (ENTER), January 18 -20
2006, Lausanne, Switzerland.
113. 吴丽花，刘鲁, “ 个性化推荐系统用户建模技术综述”, 2006.
114. 崔林，宋瀚涛，龚永罡，陆玉昌, ―基于 Web 使用挖掘的个性化服务技术研究‖ ,
2005.
115. Frina Albertyn, Fleur Fritz, Phuong Nguyen, Sergiy Zlatkin, Alastair Tennant, Roland
Kaschek, ―Towards Engineering Clinical Pathways‖, 2005.
116. Gustavo de la Cruz Martìnez, Fernando Gamboa Rodrìguez, ―Exploración Del
Aprendizaje De Los Estudiantes Haciendo Uso De Ambientes Colaborativos: Enseñando
Inteligencia Artificial‖ (Exploring the student‘s learning using collaborative
environments: teaching artificial intelligence), RIED – Revista Iberoamericana de
Educación a Distancia Volumen 8, No 1 & 2, pp. 147-158, 2005.
117. JDV Silva, ―Extracting knowledge from data originated in web sites‖, University of
Chile, 2005.
118. M. Chau, X. Fang and O. R. Liu Sheng, ―Analysis of the query logs of a Web site search
engine‖, Journal of the American Society for Information Science and Technology, vol.
56, no. 13, pp. 1363 – 1376, August 2005.
119. C. K. Georgiadis, I. Mavridis and A. Manitsaris, ―Context-Based Humanized and
Authorized Personalization in Mobile Commerce Applications‖, International Journal of
Computing and Information Systems, v. 3, n. 2, pp. 1-9, 2005.
7
120. S. D. Afantenos, V. Karkaletsis and P. Stamatopoulos, ―Summarization from Medical
Documents: A Survey‖, Journal of Artificial Intelligence in Medicine, v. 33, iss. 2, pp.
157-177, 2005.
121. J. Fuernkranz, ―Web Mining‖, In Data Mining and Knowledge Discovery Handbook, O.
Maimon and L. Rokach (eds.), pp. 899-920, Springer-Verlag, 2005.
122. J.J Jung, ―Semantic preprocessing of Web request streams for Web usage mining‖,
Journal of Universal Computer Science 11 (8), pp. 1383-1396, 2005.
123. L. Liao and T. Xiao, ―Data Mining in Parametric Product Catalogs‖, Proceedings of the
9th International Conference on Knowledge-Based Intelligent Information and
Engineering Systems (KES), Lecture Notes in Computer Science, 3681, pp. 353-358,
2005.
124. I.-H. Ting, C. Kimble and D. Kudenko, ―A pattern restore method for restoring missing
patterns in server side clickstream data‖, Proceedings of the 7th Asia-Pacific Web
Conference (APWeb), Lecture Notes in Computer Science, 3399, pp. 501-512, 2005.
125. R. Girardi, L.B. Marinho, I.R. De Oliveira, ―A system of agent-based software patterns
for user modeling based on usage mining‖, Interacting with Computers, 17 (5), pp. 567591, 2005.
126. E. Frias-Martinez, G. Magoulas, S. Chen and R. Macredie, ―Modeling human behavior in
user-adaptive systems: Recent advances using soft computing techniques‖, Expert
Systems with Applications, n. 29, pp. 320–329, 2005.
127. Kristofic and M. Bielikova, ―Improving Adaptation in Web Based Educational
Hypermedia by means of Knowledge Discovery‖, Proceedings of the 16th ACM
Conference on Hypertext and Hypermedia (HT), pp. 184-192, Salzburg, Austria,
September 2005.
128. Y. Zhao, Y.Y. Yao and N. Zhong, ―Multilevel web personalization‖, Proceedings of the
4th IEEE/WIC/ACM International Conference on Web Intelligence (WI), pp. 649-652,
IEEE Press, 2005.
129. R. Girardi, L. Balby Marinho and A. Neres Lindoso, ―WUMA-Miner: An Agent-based
Design Pattern for Mining Users with Similar Navigational Behavior‖, Proceedings of the
5th Latin American Conference on Pattern Languages of Programming (SugarLoafPLoP),
Campos do Jordão, Brazil, August 2005.
130. T. Zhang, The Value of IT-Enabled Retailer Learning: Can Personalized Product
Recommendations (PPRS) Improve Customer Store Loyalty in Electronic Markets?, PhD
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1277. R. B. Almeida and V. A. F. Almeida, ―A Community-Aware Search Engine,‖
Proceedings of the ACM International Conference WWW, pp. 413- 421, New York,
USA, May 2004.
1278. M. Degemmis, O. Licchelli, P. Lopes and G. Semeraro, ―Learning Usage Patterns for
Personalized Information Access in e-Commerce‖, In User-Centered Interaction
Paradigms for Universal Access in the Information Society, C. Stary and C Stephanidis
(Eds.), Revised Selected Papers from the 8th ERCIM Workshop on User Interfaces for
All , Lecture Notes in Computer Science, 3196, pp. 133-148, 2004.
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1279. Xing DS, Shen JY, ―Efficient data mining for web navigation patterns‖,
INFORMATION AND SOFTWARE TECHNOLOGY, 46 (1): 55-63 JAN 1 2004.
1280. Nogueira L, Oliveira E. ―Brokering in electronic insurance markets‖, MULTIAGENT SYSTEMS AND APPLICATIONS III, PROCEEDINGS, LECTURE NOTES
IN ARTIFICIAL INTELLIGENCE 2691: 574-583 2003
1281. Luis Nogueira and Eugenio Oliveira. A Multi-Agent System for E-Insurance
Brokering. In Agent Technologies, Infrastructures, Tools and Applications for e-Services.
Eds. R.Kowalczyk, R.Muller, H.Tianfield, R.Unland, LNAI 2592, pp. 263-282, Springer
2003.
1282. Rodrigo B. Almeida, Virg´ýlio A. F. Almeida, ―Design and Evaluation of a Userbased Community Discovery Technique‖ International Conference on Internet
Computing 2003:pp 17-23
1283. R. B. Almeida and V. A. F. Almeida, Local Community Identification through User
Access Patterns, Technical Report (arXiv/cs/0212045), Department of Computer Science,
Universidade Federal de Minas Gerais, Brazil, 2003.
1284. L. Nogueira and E. Oliveira, ―Learning Preferences to provide Advice‖, Proceedings
of the International Conference in Concurrent Engineering (CE), Madeira Island, July
2003.
1285. L. Nogueira, E. Oliveira, ―A Multi-Agent System for E-Insurance Brokering,‖ In
Agent Technologies, Infrastructures, Tools and Applications for e-Services, Lecture
Notes in Artificial Intelligence, v. 2592, pp. 263-282 , Springer-Verlag, 2002.
1286. F. Abbattista, M. Degemmis, O. Licchelli, P. Lops, G. Semeraro and F. Zambetta,
―Improving the usability of an e-commerce web site through personalization‖,
Proceedings of the Workshop on Recommendation and Personalization in E-commerce
(MPEC), 2nd International Conference on Adaptive Hypermedia and Adaptive Web
Based Systems (AH), Malaga, Spain, 2002.
Zambetta, Learning User Profiles for Content-Based Filtering in e-Commerce,
1288. E David, S Kipnis, S Kraus, D Richardson , ―Apparatus and method for agent-based
feedback collection in a data broadcasting network‖, US Patent 6,449,632, 2002, 2002.
1289. Z. Wang, Collaborative Filtering Using Error-Tolerant Fascicles, MSc Thesis, School
of Computing Science, Simon Fraser University, March, 2001.
1290. E. David and S. Kraus, Agents for information broadcasting In: N.R. Jennings and Y.
Lesperance, Editors, Intelligent Agents VI (LNAI Volume 1757), Springer, Berlin,
Germany (2000), pp. 91–105.
1291. M.F. Costabile, F. Esposito, G. Semeraro, N. Fanizzi, ―An adaptive visual
environment for digital libraries‖, International Journal of Digital Libraries (1999) 2: 124143.
1292. Semeraro, G., Costabile, M.F., Esposito, F., Fanizzi, N., Ferilli, S.: Machine Learning
Techniques for Adaptive User Interfaces in a Corporate Digital Library Service. Machine
Learning and Applications. In: Proceedings of the ACAI-99 Workshop on Machine
Learning in User Modeling, Chania, Crete, Greece, pp. 21–29 (1999)
Benaki, E., Karkaletsis, V. and Spyropoulos, C.D. ―Integrating User Modeling
into Information Extraction: the UMIE Prototype‖, in Proceedings of the 6th
International Conference on User Modeling (UM97), CISM No 383, Springer
Wien New York, 1997, pp. 55-58, 1997. (cited by 10)
1293. Hamza Hydri Syed, Periklis Andritsos ―User Preference Modeling - A Survey
Report‖, University of Trento, Italy, August 2007
1294. Hamza H. Syed, Periklis Andritsos ―A Lightweight Tree Structure to Model User
Preferences‖ , University of Trento, Italy, 2007
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1295. Agnieszka Indyka – Piasecka ―Model Użytkownika W Internetowych Systemach
Wyszukiwania Informacji‖, 2006
1296. T. Poibeau, ―Mixing technologies for Intelligent Information Extraction,‖ in
Proceedings of the workshop on Intelligent Information Integration, 16th International
Joint Conference on Artificial Intelligence, pp. 116–121,1999
1297. W Abramowicz, PJ Kalczynski, K Wecel - Complementing the data warehouse with
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contents, 2002
1298. C Papatheodorou - Machine Learning and Its Applications, 2001, Machine Learning
in User Modeling , Springer Lecture Notes In Artificial Intelligence, 2001
1299. D Bueno, AA David, METIORE: A Personalized Information Retrieval System.User
Modeling, 2001 - Springer Page 1. M. Bauer, PJ Gmytrasiewicz, and J. Vassileva (Eds.):
UM 2001, LNAI 2109, pp. 168–177, 2001.
1300. M.Wallace,I.Maglogiannis, K.Karpouzis, G.Kormentzas, S.Kollias, Intelligent OneStop-Shop Travel Recommendations Using An Adaptive Neural Network And Clustering
Of History, Information Technology & Tourism, Vol. 6 pp. 181–193
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Applied System Studies, vol. 1 n. 2, 2000.
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version
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report
http://www-mmt.inf.tudresden.de/joerding/Www7/www.html .
Benaki, E., Karkaletsis, V., and Spyropoulos, C.D. ―User Modeling in WWW:
the UMIE Prototype‖, in Proceedings of the Workshop "Adaptive Systems and
User Modeling on the World Wide Web", in the 6th International Conference on
User Modeling, Sardinia, Italy, June 2, 1997, pp. 13-21. (cited by 3)
1303. Agnieszka Indyka – Piasecka ―MODEL UŻYTKOWNIKA W INTERNETOWYCH
SYSTEMACH WYSZUKIWANIA INFORMACJI‖, 2006.
1304. M. Regina, M. Braga C. M. L. Werner, M. Mattoso, ―Using Ontologies for Domain
Information Retrieval‖, Proceedings of DEXA 2000.
1305. A.Sallis, R.Pascoe, ―An Evaluation Methodology for the Analysis of Dialogue
between a Student and an Adaptive Course‖, Proc. of the 7th International Conference on
Computers in Education, Chiba, 1999.
Kokkotos S., Spyropoulos C.D. ―An Architecture for Designing Internationalized
Software‖, in Proceedings of 18th International Workshop on Software
Technology and Engineering Practice (STEP‘97), July 14-18, London, pp.13-21.
(cited by 1)
1306. Stathis,K. and Sergot,M., 1998. ―An abstract framework for globalising interactive
systems‖. In Special Issue of Interacting with Computers: The Interdisciplinary Journal of
Human Computer Interaction, 9 (1998), pp. 401-416.
Spyropoulos C.D., Kokkotos S., Marinagi C. ―Planning and Scheduling Patient
Tests in Hospital Laboratories‖, in Lecture Notes in Artificial Intelligence
(LNAI), Artificial Intelligence in Medicine, no 1211, E. Keravnou, C. Carbay, R.
Band, J. Wyatt (Eds), 1997, pp. 307-318. (cited by 1)
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1307. Vassilacopoulos G, Paraskevopoulou E, ―A process model basis for evolving hospital
information systems ―, J MED SYST 21 (3): 141-153 JUN 1997
Vouros, G., Karkaletsis, E., and Spyropoulos, C.D., 1997. ― Documentation and
Translation", in ―Software without frontiers‖, ed. By P.A.Hall and R.Hudson,
J.Wileys&Sons, London, 1997 (ISBN 0 471 96974 5), pp. 167-202. (cited by 1)
1308. Day, D.L., 1998. ―Shared values and shared interfaces: The role of culture in the
globalisation of human-computer systems‖. In Special Issue of Interacting with
Computers: The Interdisciplinary Journal of Human Computer Interaction, 9 (1998), pp.
269-274.
Karkaletsis, V., Spyropoulos, C.D., Benaki, E. ―Customising Information
Extraction Templates according to Users Interests‖, in Proceedings of the
Workshop ―Lexically Driven Information Extraction - LDIE‘97‖, Frascatti, Rome,
July 16, 1997, pp 23-37. (cited by 3)
1309. Th. Poibeau, ―A corpus-based approach to Information Extraction‖, In Journal of
Applied System Studies, vol. 1 n. 2,2000
1310. O. Glickman, R.Jones, ―Examine Machine Learning for Adaptable End-to-End
Information Extraction, July 19, 1999, Florida.
1311. T. Poibeau, ―Mixing technologies for Intelligent Information Extraction,‖ in
Proceedings of the workshop on Intelligent Information Integration, 16th International
Joint Conference on Artificial Intelligence, pp. 116–121,1999
Spyropoulos, C.D., and Karkaletsis, V. "On line Generation of Messages: A
Knowledge-based Approach‖, in Proceedings of the ECAI-96 Workshop on
Multilinguality in Software Industry: The AI Contribution (MULSAIC‘96),
Budapest, Hungary, 12 August 1996. (cited by 1)
1312. Bateman, J.A., 1997, ―Enabling technology for multilingual natural language
generation: the KPML development environment‖. In Journal of Natural Language
Engineering 3(1): 15-55.
Kokkotos S., Spyropoulos C.D. ―A Framework for Developing Temporal
Databases‖, Computer Science Lecture Notes Series, Springer-Verlag, no. 856,
1994, pp. 236-245. (cited by 1)
1313. Panayiotopoulos T., Gergatsoulis M. ―Intelligent Information Processing using
TRLi‖, Workshop Proceedings of the Database and Expert Systems Applications
DEXA'95 Conference, London, U.K., Revell N. and Tjoa A.M. (editors), pp. 494-501,
Sept. 1995.
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Spyropoulos C.D., Kokkotos S. ―Interactive Fuzzy Scheduling Using the Time
Graph System TGS‖, Proceedings of the AAAI SIGMAN Workshop on
Manufacturing Scheduling, IJCAI-89 Conference, Detroit, IL, Aug. 1989. (cited
by 1)
1314. Kempf K., Le Pape C., Smith S.F. Fox. B.R. ―Issues in the Design of AI-Based
Schedulers: A Workshop Report‖, AI Magazine, Vol. 11, No. 5, pp. 37-46, Jan. 1991.
Γημοζιεύζεις ζε Δλληνικά Σσνέδρια
V. Karkaletsis and C.D. Spyropoulos. "Cross-lingual Information Management
from Web pages", Proceedings of the 9th Panhellenic Conference in Informatics
(PCI-2003), Thessaloniki, 21-23 November, 2003(cited by 1)
1315. Paliouras, G. (2005). On the need to bootstrap ontology learning with extraction
grammar learning. In Conceptual Structures: Common Semantics for Sharing Knowledge,
13th International Conference on Conceptual Structures , volume 3596 of Lecture Notes
in Computer Science, pages 119_135. Springer
Androutsopoulos I., Spiliotopoulos D., Stamatakis K., Dimitromanolaki A.,
Karkaletsis V. and Spyropoulos C.D. 2002. Symbolic authoring for multilingual
natural language generation. In Proceedings of the 2nd Hellenic Conference on
Artificial Intelligence (SETN-02), Thessaloniki, Greece (cited by 3)
1316. G Xydas, D Spiliotopoulos, G Kouroupetroglou ―Prosody Prediction from
Linguistically Enriched Documents Based on a Machine Learning Approach‖ , in
Proceedings of the 6th International Conference of Greek Linguistics (6 th
ICGL ) , Rethymno, Greece, September 18-21 2003.
1317. G Xydas, D Spiliotopoulos, G Kouroupetroglou, ―Building Prosodic Structures in a
Concept-to-Speech System‖ , In Workshop on Balkan Language Resources and Tools,
Thessaloniki, Greece, November 21, 2003.
1318. Aurelien MAX, ―De la creation de documents normalices µa la normalisation de
documents en domaine contrain‖, 2003
D. Pierrakos, G. Paliouras, C. Papatheodorou and C.D. Spyropoulos,
―KOINOTITES: A Web Usage Mining Tool for Personalization.‖ Proceedings of
the Panhellenic Conference on Human Computer Interaction (PC-HCI), pp. 231236, Patras, 2001. (cited by 5)
1319. G. Castellanoa, A.M. Fanellia and M.A. Torsello. ―NEWER: A system for NEurofuzzy WEb Recommendation‖. Applied Soft Computing, 2010
1320. Devdutta Bhosale, ―AlcoZone: An Adaptive Hypermedia Based Personalized
Alcohol Education‖, Thesis submitted to the faculty of the Virginia Polytechnic Institute
and State University in partial fulfillment of the requirements for the degree of Master of
Science in Computer Engineering, May 8,2006
1321. P. Germanakos, C. Mourlas, C. Panayiotou and G. Samaras, ―Personalization
Systems and Processes Review based on a Predetermined User Interface
Categorization‖, Proceedings of the 3rd International Congress on Communication and
Reality (CICR), pp. 431-444, 2005.
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1322. S. W. Schilke, U. Bleimann, S. M. Furnell and A. D. Phippen, ―Multi-dimensional
personalisation for location and interest-based recommendation‖, Internet Research,
Volume 14, Number 5, pp. 379-385, Emerald Group Publishing Limited, 2004.
1323. M. Koutri, S. Daskalaki, ―Improving web site usability through a clustering
approach,‖ Proceedings of the 10th International Human-Computer Interaction
Conference (HCII‘03), vol 1, pp. 788-792, Heraklion, Greece, 2003.
D. Farmakiotou, V. Karkaletsis, G. Samaritakis, G. Petasis, and C.D.
Spyropoulos ―Named Entity Recognition in Greek Web Pages‖, Proceedings
Companion Volume of 2nd Hellenic Conference on AI (SETN-02), I.P. Vlahavas
and C.D. Spyropoulos (eds), pp. 91-102, Thessaloniki, Greece, 2002.(cited by 2)
1324. G. Lucarelli, X.Vasilakos and I. Androutsopoulos ―Named Entity Recognition In
Greek Texts With An Ensemble Of SMVS And Active Learning‖, International Journal
on Artificial Intelligence Tools, vol. 16, No 6, pp-1015-1045, 2007.
1325. Georgios Lucarelli and Ion Androutsopoulos ―A Greek Named-Entity Recognizer
That Uses Support Vector Machines and Active Learning‖,
Springer Berlin /
Heidelberg, In ―Lecture Notes in Computer Science‖, Advances in Artificial Intelligence,
Volume 3955, pp 203-213, 2006.
D. Farmakiotou, V. Karkaletsis, J. Koutsias, G. Sigletos, C.D. Spyropoulos and
P. Stamatopoulos, "Rule-based Named Entity Recognition for Greek Financial
Texts", Proceedings of the Workshop on Computational lexicography and
Multimedia Dictionaries (COMLEX 2000), pp. 75-78, Patras, Greece, September
2000. (cited by 12)
1326. Hongjian Liu, Defeng Guo, Quan Zhou, Kenji Nagamatsu, Qinghua Sun, ―A PreIdentification Method for Chinese Named Entity Recognition‖, Journal of Software, Vol
5, No 1 (2010), 73-80, Jan 2010
1327. Asif Ekbal and Sivaji Bandyopadhyay. ―Named Entity Recognition Using
Appropriate Unlabeled Data, Post-processing and Voting‖. Informatica 34 (2010) 55–76
1328. Uyar, E. (2009). Near-Duplicate News Detection Using Named Entities. Master Thesis,
Computer Engineering Department, Bilkent University. Retrieved June 21, 2009 from
http://www.cs.bilkent.edu.tr/~canf/bilir_web/theses/erkanUyarThesis.pdf
1329. Gersende Georg, Hugo Hernault, Marc Cavazza, Helmut Prendinger and Mitsuru
Ishizuka
. ―From rhetorical structures to document structure: shallow pragmatic
analysis for document engineering‖. Document Engineering. Proceedings of the 9th ACM
symposium on Document engineering. Munich, Germany. Pages: 185-192, 2009
1330. Sim~oes, G., Galhardas, H., & Coheur, L. (2009). Information extraction tasks: a
survey (INESC{ID technical report No. 37/2009). Lisbon, Portugal.
1331. Georgios Lucarelli and Ion Androutsopoulos ―A Greek Named-Entity Recognizer
That Uses Support Vector Machines and Active Learning‖,
Springer Berlin /
Heidelberg, In ―Lecture Notes in Computer Science‖, Advances in Artificial Intelligence,
Volume 3955, pp 203-213, 2006.
1332.
Gulila Altenbek , ―Rule-based Person Name Recognition for Xinjiang Minority
Languages.‖, Journal of Chinese Language and Computing 15 (4) pp 219-226 , 2005
1333. Gulila Adongbieke, ―Rule-based Person-name Recognition for Uighur Texts‖, In
ICCC 2005
1334.
Andrew Smith, Trevor Cohn, and Miles Osborne. 2005. Logarithmic opinion pools
for conditional random _elds. In Proceedings of ACL 2005.
1335. Indra Budi, Stéphane Bressan, Gatot Wahyudi, Zainal A. Hasibuan, Bobby Nazief:
―Named Entity Recognition for the Indonesian Language: Combining Contextual,
Morphological and Part-of- Speech Features into a Knowledge Engineering Approach‖.
Discovery Science 2005.
1336. Petya Osenova and Sia Kolkovska, ―Combining the named-entity recognition task
and NP chunking strategy for robust pre-processing‖ In: Proc. of The First Workshop on
74
Treebanks and Linguistic Theories (TLT2002), 20th and 21st September 2002, Sozopol,
Bulgaria. pages 167-182.
1337. Dalianis and E. Astrom, ―SweNam - A Swedish Named Entity Recogniser - Its
construction, training and evaluation‖, TRITA-NA-P0113, IPLab-189, July 2001
(http://www.nada.kth.se/~hercules/papers/SweNam.pdf).
A2. EDUCATIONAL CITATIONS
I. Androutsopoulos, J. Koutsias, K.V. Chandrinos, G. Paliouras, and C.D. Spyropoulos, "An
Evaluation of Naive Bayesian Anti-Spam Filtering". Proceedings of the Workshop
on Machine Learning in the New Information Age, 11th European Conference on
Machine Learning (ECML), pp. 9-17, Barcelona, Spain, 2000. [15 Citations]
1. I. Schuyt, Evolutionary Spam Filter, Project Report for the course project (CPSC503),
Υπεύθσνος Καθηγηηής: J. Denzinger, Department of Computer Science, University of
Calgary, Canada, 2005.
2. : R. Goetschi, SPAM-Filtering using Artificial Neural Networks, Semester Thesis,
Υπεύθσνος Καθηγηηής: Β. Anrig, Department of Computer Science, Berne University of
Applied Sciences, Switzerland, July 2004.
3. : D. C. Trudgian. SpamKANN: A k-Nearest Neighbour Spam Filter. Indiviudual Project
Report for COM3401, Department of Computer Science, University of Exeter, UK, 2004.
4. : M. Fernal, Mailsahv: Email Statistical Algorithmic Hybrid Virus Filter, Project report for
the course Information Systems Security (CSC574), Υπεύθσνος Καθηγηηής: Τ. Yu,
Department of Computer Science, North Carolina State University, USA, 2005.
5. Φρήζη από ηοσς καθ. I. Korpinska & J. Patrick ζηο μάθημα Knowledge Discovery and
Data Mining (COMP5318), School of Information Technologies, University of Sydney,
2004.
6. : T. Stone. Parameterization of naive bayes for spam filters, Masters comprehensive exam,
Department of Computer Science, University of Colorado at Boulder, USA, 2003.
7. Φρήζη από ηοσς καθ. G. Klinker & S. Kramer ζηο μάθημα Machine Learning, Institute
fuer Informatik, TU Muenchen, Germany, 2003.
8. : T. Chen, D. Chen and H. Ming, Spam Email Filtering, Project report for the course
Principles of Artificial Intelligence (ComS572), Instructor: Dimitris Margaritis,
Department of Computer Science, Iowa State University, USA, 2003.
9. M. Ghosh and J. J. Guernsey, Exploration of Neuro-Fuzzy Spam Filtering based on Naive
Bayesian Filters, Project report for the course Knowledge Based Systems (EECS 6360),
Instructor: Devinder Kaur, Electrical Engineering and Computer Science Department,
University of Toledo, USA, 2003.
10.: A. Bratko, Hierarhično razvrščanje elektronske pošte z metodami strojnega učenja, BSc
Thesis, Υπεύθσνος καθηγηηής: B. Zupan, Faculty of Computer and Information Science,
University of Ljubljana, 2003.
11.: R. Drewes, An artificial neural network spam classifier, Project report for the course on
Artificial Intelligence (CS676) , University of Nevada at Rino, USA (2002).
12.: N. Street, F. Menczer, P. Srinivasan and S. Bradshaw ITR: PREJEC T -- Peer-based
Rejection of Email Junk via Ensemble Classification Techniques, NSF Project Proposal
(2003).
13.: H. Stern. Optimizing Naive Bayesian Networks for Spam Detection, Project report for the
course on Natural Language Processing (CSCI 6509), Dalhousie University, Halifax, NS,
Canada, 2002.
14.: Q. Shen, Fuzzy-rough anti-spam Filtering, Postgrad dissertation proposal, Department of
Artificial Intelligence, University of Edinburgh (2002).
75
15.: S. Crosby and S. Byrd, Email Classification through Graph Analysis, Project report for
the Course Computer Systems Security (COMP527), Department of Computer Science,
Rice University, USA, 2002.
I. Androutsopoulos, G. Paliouras, V. Karkaletsis, G. Sakkis, C.D. Spyropoulos and P.
Stamatopoulos. ―Learning to Filter Spam E-Mail: A Comparison of a Naive
Bayesian and a Memory-Based Approach‖. Proceedings of the Workshop “Machine
Learning and Textual Information Access”, European Conference on Principles and
Practice of Knowledge Discovery in Databases (PKDD), pp. 1-13, Lyon, France, 2000.
[14 ]
16.: M. Fischer Christensen and M. Lobner-Olesen, Using Multiple Feature Selection
Methods in Ensembles, Project Report, Supervisor: Ole Fogh, IT University of
Copenhagen, Denmark, September 2005.
17.: Jennifer Lee, SPAM Filter, Assignment for the course on Advanced Artificial
Intelligence Concepts (15-780), Computer Science Department, Carnegie Mellon
University, USA (2004).
18.: K. Tretyakov, Machine Learning Techniques in Spam Filtering, Data Mining Problemoriented Seminar, MTAT.03.177, pp. 60-79, Institute of Computer Science, University of
Tartu, Estonia, May 2004.
19.: Eric Stiles, Ba.S.E. Filter, Project report for the course on Knowledge Based Systems
(CSCI8050), Computer Science Department, The University of Georgia, USA (2003).
20.: T. Chen, D. Chen and H. Ming, Spam Email Filtering, Project report for the course
Principles of Artificial Intelligence (ComS572), Instructor: Dimitris Margaritis,
Department of Computer Science, Iowa State University, USA, 2003.
21.: N. Tyrone, Using AdaBoost and Decision Stumps to Identify Spam E-mail, Course report
Inteligencia Artificial (MC906), Υπεύθσνος μαθήμαηος: Jacques Wainer, 2003.
22. Used in the course: Machine Learning and Knowledge Discovery (CS674), Department of
Computer Science & Engineering, Indian Institute of Technology Kanpur (2003).
23.: A. S. Menz, An Analysis of Methods in Feature Selection and Classification Algorithms
for Spam Detection, Project Report for the Machine Learning Course (CS573), Department
of Computer Science, Iowa State University, 2002.
24.: H. Stern. Optimizing Naive Bayesian Networks for Spam Detection, Project report for the
course on Natural Language Processing (CSCI 6509), Dalhousie University, Halifax, NS,
Canada, 2002.
25.: F. Trepanier, Learning to Filter Spam E-Mail, Presentation for Data mining and Machine
Learning seminar course (CS760), Department of Electrical & Electronic Engineering, The
University of Auckland, New Zealand (2002).
26. Paper translated by Benjamin Becquet and Gaelle Loosli in French and used in their
course work Filtre de mails: Comparaison entre le classifieur de Bayes naοf et le
classifieur knn, for the course: UV Reseaux de Neurones, Reseaux Bayesiens et
Applications. Instructor: Philippe Leray, Department of Information Systems Engineering,
INSA Rouen.
27.Part of the paper translated in Slovenian and used in the postgraduate course ―Machine
Learning‖ (2001) Instructor: Igor Kononenko, School of Computer Science and
Information Systems, Univ. of Ljubljana, Slovenia.
28.: M. DeSouza, J. Fitzgerald, C. Kemp, G. Truong, A Decision Tree based Spam Filtering
Agent, Project Report for the course Knowledge representation and reasoning, Department
of Computer Science, University of Melbourne, 2001.
29.: P. Leray, Seminar Reseaux de neurones, Reseaux Bayesiens et Applications Le Projet
ASI4 le forum MATLAB, Department of Information Systems Engineering, INSA Rouen.
G. Paliouras, C. Papatheodorou, V. Karkaletsis and C.D. Spyropoulos, ―Clustering the
Users of Large Web Sites into Communities,‖ Proceedings of the International
Conference on Machine Learning (ICML), pp. 719-726, Stanford, California, 2000. [5
Citations ]
76
30. Used by the teaching Group of: Web Usage Mining Journal Club, Computing Science
Department, University of Alberta, Canada.
31.: Tingshao Zhu, Web Usage Mining for Internet Recommendation, Proposal for Ph.D
Candidacy Examination, Computing Science Department, University of Alberta, Canada.
32.Paper translation in Finish and used in a serious of seminars entitled ―Mining knowledge
from data‖ (2002) Instructor: Hannu Toivonen, Dept. of Informatics, Univ. of Helsinki,
Finland.
33. Used in the course ―Knowledge Mining from the Wel‖ (ECT 584 / CSC 594, 2002)
Instructor: Bamshad Mobasher, Dept. of Computer, Telecommunication and Information
Systems, Univ. of De Paul, USA
34. Used by the research candidate Gabriela Tissiani, PhD Student in Engenharia de Sistemas
na area de Midia, UFSC - Federal University of Santa Catarina - Florianopolis, SC, Brazil.
Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C.D. Spyropoulos and P.
Stamatopoulos, ―A Memory-Based Approach to Anti-Spam Filtering for Mailing
Lists,‖ Information Retrieval, v. 6, n. 1, pp. 49-73, 2003. [4 Citations ]
35.Ph.D. research proposal entitled: ―Evolving by Forgetting: Adaptive Text Classification‖,
επιβλέπονηες N. Wiratunga, I. Koychev, D. Harper, School of Computing, Robert Gordon
University, Aberdeen, UK, 2004.
36.: D. C. Trudgian. SpamKANN: A k-Nearest Neighbour Spam Filter. Indiviudual Project
Report for COM3401, Department of Computer Science, University of Exeter, UK, 2004.
37.: T. Stone. Parameterization of naive bayes for spam filters. Masters comprehensive exam,
Department of Computer Science, University of Colorado at Boulder, USA, 2003.
38.: A. Bratko, Hierarhično razvrščanje elektronske pošte z metodami strojnega učenja, BSc
Thesis, Υπεύθσνος καθηγηηής: B. Zupan, Faculty of Computer and Information Science,
University of Ljubljana, 2003.
G. Paliouras, V. Karkaletsis, C. Papatheodorou and C.D. Spyropoulos, ―Exploiting Learning
Techniques for the Acquisition of User Stereotypes and Communities,‖
Proceedings of the International Conference on User Modelling (UM), CISM Courses
and Lectures, n. 407, pp. 169-178, Springer-Verlag, 1999. [2 Citations ]
39.Used n a course work by Amelie Cordier, DEA, DISIC, INSA, Lyon, France (2003).
40.Used by the teaching Group of: Web Usage Mining Journal Club, Computing Science
G. Sakkis, I. Androutsopoulos, G. Paliouras, V. Karkaletsis, C.D. Spyropoulos and P.
Stamatopoulos, ―Stacking classifiers for anti-spam filtering of e-mail.‖ Proceedings
of the International Conference on Empirical Methods in Natural Language Processing
(EMNLP), pp. 44-50, Carnegie Mellon University, 2001. [2 Citations ]
41.: M. Fischer Christensen and M. Lobner-Olesen, Using Multiple Feature Selection
Methods in Ensembles, Project Report, Supervisor: Ole Fogh, IT University of
Copenhagen, Denmark, September 2005.
42.: T. Sorvik, Improving Spam Filtering by Training on Artificially Generated Text, Project
Report for MS051 (Machine Learning and Data Mining), Supervisor: Tim Kovacs,
Department of Computer Science, University of Bristol, UK, May 2004.
. D. Pierrakos, G. Paliouras, C. Papatheodorou and C.D. Spyropoulos, ―Web Usage Mining
as a tool for personalization: a survey,‖ User Modeling and User-Adapted
Interaction, v. 13, n. 4, pp. 311-372, November 2003. [1 Citation ]
43. Used in the course Adaptive Web-based Information Systems ηοσ K. Meissner, Fakultaet
Informatik, Technische Universitaet Dresden, Germany, 2004.
G. Paliouras, V. Karkaletsis, I. Androutsopoulos, and C.D. Spyropoulos, "Learning Rules
for Large-Vocabulary Word Sense Disambiguation: A Comparison of Various
77
Classifiers". Proceedings of the International Conference on Natural Language
Processing (NLP), Lecture Notes in Artificial Intelligence, n. 1835, pp. 383-394,
Springer, 2000. [1 Citation ]
44.:Y. Chen The Comparative Analysis about the classification algorithms and the data sets,
Project report for the MSc course on Machine Learning, Department of Computer Science,
University of Huston, 2002.
G. Paliouras, V. Karkaletsis and C.D. Spyropoulos, ―Learning Rules for Large Vocabulary
Word Sense Disambiguation,‖ Proceedings of the International Joint Conference on
Artificial Intelligence (IJCAI '99), v. 2, pp. 674-679, 1999. [1 Citation ]
45.Αναθορά: C. Demwell, Human Information Access with Fuzzy Searching, Fuzzybase
Directed Study, Communication Networks Laboratory, School of Engineering Science,
Simon Fraser University, 2001.
G. Paliouras, C. Papatheodorou, V. Karkaletsis, C.D. Spyropoulos and V. Malaveta,
―Learning User Communities for Improving the Services of Information Providers,‖
Proceedings of the European Conference on Research and Advanced Technology for Digital
Libraries (ECDL), Lecture Notes in Computer Science, n. 1513, pp. 367-384, SpringerVerlag, 1998. [1 Citation ]
46. Used by the teaching Group of: Web Usage Mining Journal Club, Computing Science
G. Paliouras, V. Karkaletsis and C.D. Spyropoulos (editors), Machine Learning and
Applications. Lecture Notes in Computer Science, n. 2049, Springer-Verlag, 2001. [1
Citation ]
47. Used in the course CSI 873: Computational Learning and Discovery ηοσ R. Michalski,
Machine Learning Institute, George Mason University, USA, 2004.
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