06207nam 22006855 450 991014387470332120200704230318.03-540-36755-110.1007/3-540-36755-1(CKB)1000000000212010(SSID)ssj0000324581(PQKBManifestationID)11251060(PQKBTitleCode)TC0000324581(PQKBWorkID)10314460(PQKB)10587545(DE-He213)978-3-540-36755-0(MiAaPQ)EBC3073102(PPN)155213520(EXLCZ)99100000000021201020121227d2002 u| 0engurnn#008mamaatxtccrMachine Learning: ECML 2002 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings /edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen1st ed. 2002.Berlin, Heidelberg :Springer Berlin Heidelberg :Imprint: Springer,2002.1 online resource (XIV, 538 p.)Lecture Notes in Artificial Intelligence ;2430Bibliographic Level Mode of Issuance: Monograph3-540-44036-4 Includes bibliographical references and index.Contributed Papers -- Convergent Gradient Ascent in General-Sum Games -- Revising Engineering Models: Combining Computational Discovery with Knowledge -- Variational Extensions to EM and Multinomial PCA -- Learning and Inference for Clause Identification -- An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks -- Variance Optimized Bagging -- How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code -- Sparse Online Greedy Support Vector Regression -- Pairwise Classification as an Ensemble Technique -- RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood -- Using Hard Classifiers to Estimate Conditional Class Probabilities -- Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear Learner -- Scaling Boosting by Margin-Based Inclusion of Features and Relations -- Multiclass Alternating Decision Trees -- Possibilistic Induction in Decision-Tree Learning -- Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov Chains -- Collaborative Learning of Term-Based Concepts for Automatic Query Expansion -- Learning to Play a Highly Complex Game from Human Expert Games -- Reliable Classifications with Machine Learning -- Robustness Analyses of Instance-Based Collaborative Recommendation -- iBoost: Boosting Using an instance-Based Exponential Weighting Scheme -- Towards a Simple Clustering Criterion Based on Minimum Length Encoding -- Class Probability Estimation and Cost-Sensitive Classification Decisions -- On-Line Support Vector Machine Regression -- Q-Cut—Dynamic Discovery of Sub-goals in Reinforcement Learning -- A Multistrategy Approach to the Classification of Phases in Business Cycles -- A Robust Boosting Algorithm -- Case Exchange Strategies in Multiagent Learning -- Inductive Confidence Machines for Regression -- Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique -- Propagation of Q-values in Tabular TD(?) -- Transductive Confidence Machines for Pattern Recognition -- Characterizing Markov Decision Processes -- Phase Transitions and Stochastic Local Search in k-Term DNF Learning -- Discriminative Clustering: Optimal Contingency Tables by Learning Metrics -- Boosting Density Function Estimators -- Ranking with Predictive Clustering Trees -- Support Vector Machines for Polycategorical Classification -- Learning Classification with Both Labeled and Unlabeled Data -- An Information Geometric Perspective on Active Learning -- Stacking with an Extended Set of Meta-level Attributes and MLR -- Invited Papers -- Finding Hidden Factors Using Independent Component Analysis -- Reasoning with Classifiers -- A Kernel Approach for Learning from almost Orthogonal Patterns -- Learning with Mixture Models: Concepts and Applications.This book constitutes the refereed preceedings of the 13th European Conference on Machine Learning, ECML 2002, held in Helsinki, Finland in August 2002. The 41 revised full papers presented together with 4 invited contributions were carefully reviewed and selected from numerous submissions. Among the topics covered are computational discovery, search strategies, Classification, support vector machines, kernel methods, rule induction, linear learning, decision tree learning, boosting, collaborative learning, statistical learning, clustering, instance-based learning, reinforcement learning, multiagent learning, multirelational learning, Markov decision processes, active learning, etc.Lecture Notes in Artificial Intelligence ;2430Artificial intelligenceAlgorithmsMathematical logicArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Algorithm Analysis and Problem Complexityhttps://scigraph.springernature.com/ontologies/product-market-codes/I16021Mathematical Logic and Formal Languageshttps://scigraph.springernature.com/ontologies/product-market-codes/I16048Artificial intelligence.Algorithms.Mathematical logic.Artificial Intelligence.Algorithm Analysis and Problem Complexity.Mathematical Logic and Formal Languages.006.3/1Elomaa Tapioedthttp://id.loc.gov/vocabulary/relators/edtMannila Heikkiedthttp://id.loc.gov/vocabulary/relators/edtToivonen Hannuedthttp://id.loc.gov/vocabulary/relators/edtEuropean Conference on Machine LearningMiAaPQMiAaPQMiAaPQBOOK9910143874703321Machine Learning: ECML 20022287416UNINA