UID:
almahu_9947920565202882
Format:
XVI, 508 p.
,
online resource.
ISBN:
9783540398042
Series Statement:
Lecture Notes in Computer Science, 2838
Content:
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.
Note:
Invited Papers -- From Knowledge-Based to Skill-Based Systems: Sailing as a Machine Learning Challenge -- Two-Eyed Algorithms and Problems -- Next Generation Data Mining Tools: Power Laws and Self-similarity for Graphs, Streams and Traditional Data -- Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions -- Contributed Papers -- Efficient Statistical Pruning of Association Rules -- Majority Classification by Means of Association Rules -- Adaptive Constraint Pushing in Frequent Pattern Mining -- ExAnte: Anticipated Data Reduction in Constrained Pattern Mining -- Minimal k-Free Representations of Frequent Sets -- Discovering Unbounded Episodes in Sequential Data -- Mr-SBC: A Multi-relational Naïve Bayes Classifier -- SMOTEBoost: Improving Prediction of the Minority Class in Boosting -- Using Belief Networks and Fisher Kernels for Structured Document Classification -- A Skeleton-Based Approach to Learning Bayesian Networks from Data -- On Decision Boundaries of Naïve Bayes in Continuous Domains -- Application of Inductive Logic Programming to Structure-Based Drug Design -- Visualizing Class Probability Estimators -- Automated Detection of Epidemics from the Usage Logs of a Physicians’ Reference Database -- An Indiscernibility-Based Clustering Method with Iterative Refinement of Equivalence Relations -- Preference Mining: A Novel Approach on Mining User Preferences for Personalized Applications -- Explaining Text Clustering Results Using Semantic Structures -- Analyzing Attribute Dependencies -- Ranking Interesting Subspaces for Clustering High Dimensional Data -- Efficiently Finding Arbitrarily Scaled Patterns in Massive Time Series Databases -- Using Transduction and Multi-view Learning to Answer Emails -- Exploring Fringe Settings of SVMs for Classification -- Rule Discovery and Probabilistic Modeling for Onomastic Data -- Constraint-Based Mining of Sequential Patterns over Datasets with Consecutive Repetitions -- Symbolic Distance Measurements Based on Characteristic Subspaces -- The Pattern Ordering Problem -- Collaborative Filtering Using Restoration Operators -- Efficient Frequent Query Discovery in Farmer -- Towards Behaviometric Security Systems: Learning to Identify a Typist -- Efficient Density Clustering Method for Spatial Data -- Statistical ?-Partition Clustering over Data Streams -- Enriching Relational Learning with Fuzzy Predicates -- Text Categorisation Using Document Profiling -- A Simple Algorithm for Topic Identification in 0–1 Data -- Bottom-Up Learning of Logic Programs for Information Extraction from Hypertext Documents -- Predicting Outliers -- Mining Rules of Multi-level Diagnostic Procedure from Databases -- Learning Characteristic Rules Relying on Quantified Paths -- Topic Learning from Few Examples -- Arbogodaï, a New Approach for Decision Trees.
In:
Springer eBooks
Additional Edition:
Printed edition: ISBN 9783540200857
Language:
English
Subjects:
Computer Science
Keywords:
Konferenzschrift
URL:
http://dx.doi.org/10.1007/b13634
URL:
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