Machine Learning: Ecml 2003 : 14th European Conference on Machine Learning, Cavtat-Dubrovnik, Croatia, September 22-26, 2003 : Proceedings

by Lavrac, Nada; Lavrac, Nada; Gamberger, Dragan; Todorovsky, Ljupco; Blockeel, Hendrik

• ISBN13: 9783540201212
• ISBN10: 3540201211
• Format: Paperback
• Copyright: 2003-09-01
• Publisher: Springer-Verlag New York Inc

Summary

This book constitutes the refereed proceedings of the 14th European Conference on Machine Learning, ECML 2003, held in Cavtat-Dubrovnik, Croatia in September 2003 in conjunction with PKDD 2003. The 40 revised full papers presented together with 4 invited contributions were carefully reviewed and, together with another 40 ones for PKDD 2003, selected from a total of 332 submissions. The papers address all current issues in machine learning including support vector machine, inductive inference, feature selection algorithms, reinforcement learning, preference learning, probabilistic grammatical inference, decision tree learning, clustering, classification, agent learning, Markov networks, boosting, statistical parsing, Bayesian learning, supervised learning, and multi-instance learning.

Table of Contents

From Knowledge-Based to Skill-Based Systems: Sailing as a Machine Learning Challenge	p. 1
Two-Eyed Algorithms and Problems	p. 9
Next Generation Data Mining Tools: Power Laws and Self-similarity for Graphs, Streams and Traditional Data	p. 10
Taking Causality Seriously: Propensity Score Methodology Applied to Estimate the Effects of Marketing Interventions	p. 16
Support Vector Machines with Example Dependent Costs	p. 23
Abalearn: A Risk-Sensitive Approach to Self-play Learning in Abalone	p. 35
Life Cycle Modeling of News Events Using Aging Theory	p. 47
Unambiguous Automata Inference by Means of State-Merging Methods	p. 60
Could Active Perception Aid Navigation of Partially Observable Grid Worlds?	p. 72
Combined Optimization of Feature Selection and Algorithm Parameters in Machine Learning of Language	p. 84
Iteratively Extending Time Horizon Reinforcement Learning	p. 96
Volume under the ROC Surface for Multi-class Problems	p. 108
Improving the AUC of Probabilistic Estimation Trees	p. 121
Scaled CGEM: A Fast Accelerated EM	p. 133
Pairwise Preference Learning and Ranking	p. 145
A New Way to Introduce Knowledge into Reinforcement Learning	p. 157
Improvement of the State Merging Rule on Noisy Data in Probabilistic Grammatical Inference	p. 169
COllective INtelligence with Sequences of Actions - Coordinating Actions in Multi-agent Systems	p. 181
Rademacher Penalization over Decision Tree Prunings	p. 193
Learning Rules to Improve a Machine Translation System	p. 205
Optimising Performance of Competing Search Engines in Heterogeneous Web Environments	p. 217
Robust k-DNF Learning via Inductive Belief Merging	p. 229
Logistic Model Trees	p. 241
Color Image Segmentation: Kernel Do the Feature Space	p. 253
Evaluation of Topographic Clustering and Its Kernelization	p. 265
A New Pairwise Ensemble Approach for Text Classification	p. 277
Self-evaluated Learning Agent in Multiple State Games	p. 289
Classification Approach towards Ranking and Sorting Problems	p. 301
Using MDP Characteristics to Guide Exploration in Reinforcement Learning	p. 313
Experiments with Cost-Sensitive Feature Evaluation	p. 325
A Markov Network Based Factorized Distribution Algorithm for Optimization	p. 337
On Boosting Improvement: Error Reduction and Convergence Speed-Up	p. 349
Improving SVM Text Classification Performance through Threshold Adjustment	p. 361
Backoff Parameter Estimation for the DOP Model	p. 373
Improving Numerical Prediction with Qualitative Constraints	p. 385
A Generative Model for Semantic Role Labeling	p. 397
Optimizing Local Probability Models for Statistical Parsing	p. 409
Extended Replicator Dynamics as a Key to Reinforcement Learning in Multi-agent Systems	p. 421
Visualizations for Assessing Convergence and Mixing of MCMC	p. 432
A Decomposition of Classes via Clustering to Explain and Improve Naive Bayes	p. 444
Improving Rocchio with Weakly Supervised Clustering	p. 456
A Two-Level Learning Method for Generalized Multi-instance Problems	p. 468
Clustering in Knowledge Embedded Space	p. 480
Ensembles of Multi-instance Learners	p. 492
Author Index	p. 503
Table of Contents provided by Blackwell. All Rights Reserved.