Knowledge Discovery in Databases: PKDD 2003: 7th European Conference on Principles and Practice of Knowledge Discovery in Databases, Cavtat-Dubrovnik, Croatia, September 22-26, 2003, ProceedingsNada Lavrač Springer Science & Business Media, 2003 M09 11 - 508 pages The proceedings of ECML/PKDD2003 are published in two volumes: the P- ceedings of the 14th European Conference on Machine Learning (LNAI 2837) and the Proceedings of the 7th European Conference on Principles and Practice of Knowledge Discovery in Databases (LNAI 2838). The two conferences were held on September 22–26, 2003 in Cavtat, a small tourist town in the vicinity of Dubrovnik, Croatia. As machine learning and knowledge discovery are two highly related ?elds, theco-locationofbothconferencesisbene?cialforbothresearchcommunities.In Cavtat, ECML and PKDD were co-located for the third time in a row, following the successful co-location of the two European conferences in Freiburg (2001) and Helsinki (2002). The co-location of ECML2003 and PKDD2003 resulted in a joint program for the two conferences, including paper presentations, invited talks, tutorials, and workshops. Out of 332 submitted papers, 40 were accepted for publication in the ECML2003proceedings,and40wereacceptedforpublicationinthePKDD2003 proceedings. All the submitted papers were reviewed by three referees. In ad- tion to submitted papers, the conference program consisted of four invited talks, four tutorials, seven workshops, two tutorials combined with a workshop, and a discovery challenge. |
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Page xiv
... Approach to Learning Bayesian Networks from Data ... 132 Steven van Dijk, Linda C. van der Gaag, and Dirk Thierens On Decision Boundaries of Na ̈ıve Bayes in Continuous Domains.......... 144 Tapio Elomaa and Juho Rousu Application of ...
... Approach to Learning Bayesian Networks from Data ... 132 Steven van Dijk, Linda C. van der Gaag, and Dirk Thierens On Decision Boundaries of Na ̈ıve Bayes in Continuous Domains.......... 144 Tapio Elomaa and Juho Rousu Application of ...
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Contents
Invited Papers | 1 |
Propensity Score Methodology Applied | 16 |
Majority Classification by Means of Association Rules | 35 |
Anticipated Data Reduction in Constrained Pattern Mining | 59 |
Discovering Unbounded Episodes in Sequential Data | 83 |
Improving Prediction of the Minority Class in Boosting | 107 |
Using Belief Networks and Fisher Kernels | 120 |
On Decision Boundaries of Naıve Bayes in Continuous Domains | 144 |
Using Transduction and Multiview Learning to Answer Emails | 266 |
Exploring Fringe Settings of SVMs for Classification | 278 |
Rule Discovery and Probabilistic Modeling for Onomastic Data | 291 |
Symbolic Distance Measurements Based on Characteristic Subspaces | 315 |
Collaborative Filtering Using Restoration Operators | 339 |
Statistical σPartition Clustering over Data Streams | 387 |
Text Categorisation Using Document Profiling | 411 |
BottomUp Learning of Logic Programs for Information Extraction | 435 |
Visualizing Class Probability Estimators | 168 |
An IndiscernibilityBased Clustering Method with Iterative Refinement | 192 |
Explaining Text Clustering Results Using Semantic Structures | 217 |
Ranking Interesting Subspaces for Clustering High Dimensional Data | 241 |
Mining Rules of Multilevel Diagnostic Procedure from Databases | 459 |
Topic Learning from Few Examples | 483 |
507 | |
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accuracy algorithm analysis applications approach association attributes average called categorical cell classifier clustering compared complex computed concept Conference consider constraint contains corrected corresponding Data Mining database dataset defined denote density dependence described detection different distribution document domain episodes estimation evaluation example experiments Figure first frequent function given groups induction instance interaction interesting International itemsets Knowledge labeled learning Machine Learning means measure method mining monotone names negative node Note objects observed obtained occurrence optimal parameters patterns performance points positive possible precision prediction preference present probability problem properties proposed pruning query recall relation representation represented rules sample selected sequence shown similar space standard statistical structure Table technique threshold topic tree values variables vector