Machine Learning: ECML 2002 : 13th European Conference on Machine Learning, Helsinki, Finland, August 19-23, 2002. Proceedings / edited by Tapio Elomaa, Heikki Mannila, Hannu Toivonen.

Format:

Book

Contributor:

Elomaa, Tapio, 1963- editor.

Mannila, Heikki, editor.

Toivonen, Hannu, editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 2430.

Lecture Notes in Artificial Intelligence ; 2430

Language:

English

Subjects (All):

Artificial intelligence.

Algorithms.

Logic, Symbolic and mathematical.

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Local Subjects:

Artificial Intelligence.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Physical Description:

1 online resource (XIV, 538 pages).

Edition:

First edition 2002.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2002.

System Details:

text file PDF

Summary:

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, et cetera.

Contents:

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.

Other Format:

Printed edition:

ISBN:

978-3-540-36755-0

9783540367550

Access Restriction:

Restricted for use by site license.