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Machine Learning: Ecml 2002 : 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings - Tapio Elomaa

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Machine Learning: Ecml 2002

13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings

By: Tapio Elomaa (Editor), Heikki Mannila (Editor), Hannu Toivonen (Editor)

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Paperback | 5 August 2002

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552 Pages

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24.13 x 15.88 x 2.54

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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.

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You Can Find This Book In

Non-Fiction Computing & I.T.Computer Science Mathematical Theory of Computation Mathematics Mathematical Foundation Mathematical Logic Artificial Intelligence Machine Learning Medicine Medicine in General Computer Programming & Software Development

Algorithms & Data Structures Engineering & Technology Technology in General Engineering in General

Contributed Papers
Convergent Gradient Ascent in General-Sum Games	p. 1
Revising Engineering Models: Combining Computational Discovery with Knowledge	p. 10
Variational Extensions to EM and Multinomial PCA	p. 23
Learning and Inference for Clause Identification	p. 35
An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks	p. 48
Variance Optimized Bagging	p. 60
How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code	p. 72
Sparse Online Greedy Support Vector Regression	p. 84
Pairwise Classification as an Ensemble Technique	p. 97
RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood	p. 111
Using Hard Classifiers to Estimate Conditional Class Probabilities	p. 124
Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear Learner	p. 135
Scaling Boosting by Margin-Based Inclusion of Features and Relations	p. 148
Multiclass Alternating Decision Trees	p. 161
Possibilistic Induction in Decision-Tree Learning	p. 173
Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov Chains	p. 185
Collaborative Learning of Term-Based Concepts for Automatic Query Expansion	p. 195
Learning to Play a Highly Complex Game from Human Expert Games	p. 207
Reliable Classifications with Machine Learning	p. 219
Robustness Analyses of Instance-Based Collaborative Recommendation	p. 232
Nicholas Kushmerick iBoost: Boosting Using an instance-Based Exponential Weighting Scheme	p. 245
Towards a Simple Clustering Criterion Based on Minimum Length Encoding	p. 258
Class Probability Estimation and Cost-Sensitive Classification Decisions	p. 270
On-Line Support Vector Machine Regression	p. 282
Q-Cut - Dynamic Discovery of Sub-goals in Reinforcement Learning	p. 295
A Multistrategy Approach to the Classification of Phases in Business Cycles	p. 307
A Robust Boosting Algorithm	p. 319
Case Exchange Strategies in Multiagent Learning	p. 331
Inductive Confidence Machines for Regression	p. 345
Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique	p. 357
Propagation of Q-values in Tabular TD()	p. 369
Transductive Confidence Machines for Pattern Recognition	p. 381
Characterizing Markov Decision Processes	p. 391
Phase Transitions and Stochastic Local Search in k-Term DNF Learning	p. 405
Discriminative Clustering: Optimal Contingency Tables by Learning Metrics	p. 418
Boosting Density Function Estimators	p. 431
Ranking with Predictive Clustering Trees	p. 444
Support Vector Machines for Polycategorical Classification	p. 456
Learning Classification with Both Labeled and Unlabeled Data	p. 468
An Information Geometric Perspective on Active Learning	p. 480
Stacking with an Extended Set of Meta-level Attributes and MLR	p. 493
Invited Papers
Finding Hidden Factors Using Independent Component Analysis	p. 505
Reasoning with Classifiers	p. 506
A Kernel Approach for Learning from almost Orthogonal Patterns	p. 511
Learning with Mixture Models: Concepts and Applications	p. 529
Author Index	p. 531
Table of Contents provided by Publisher. All Rights Reserved.

Machine Learning: Ecml 2002

13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings

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Paperback

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