Machine Learning: ECML 2005: 16th European Conference on Machine Learning, Porto, Portugal, October 3-7, 2005, Proceedings (Lecture Notes in Computer Science, 3720)

✍ Scribed by João Gama (editor), Rui Camacho (editor), Pavel Brazdil (editor), Alípio Jorge (editor), Luís Torgo (editor)

• Publisher: Springer
• Year: 2005
• Tongue: English
• Leaves: 784
• Category: Library

✦ Synopsis

The European Conference on Machine Learning (ECML) and the European Conference on Principles and Practice of Knowledge Discovery in Databases (PKDD) were jointly organized this year for the ?fth time in a row, after some years of mutual independence before. After Freiburg (2001), Helsinki (2002), Cavtat (2003) and Pisa (2004), Porto received the 16th edition of ECML and the 9th PKDD in October 3–7. Having the two conferences together seems to be working well: 585 di?erent paper submissions were received for both events, which maintains the high s- mission standard of last year. Of these, 335 were submitted to ECML only, 220 to PKDD only and 30 to both. Such a high volume of scienti?c work required a tremendous e?ort from Area Chairs, Program Committee members and some additional reviewers. On average, PC members had 10 papers to evaluate, and Area Chairs had 25 papers to decide upon. We managed to have 3 highly qua- ?edindependentreviewsperpaper(withveryfewexceptions)andoneadditional overall input from one of the Area Chairs. After the authors’ responses and the online discussions for many of the papers, we arrived at the ?nal selection of 40 regular papers for ECML and 35 for PKDD. Besides these, 32 others were accepted as short papers for ECML and 35 for PKDD. This represents a joint acceptance rate of around 13% for regular papers and 25% overall. We thank all involved for all the e?ort with reviewing and selection of papers. Besidesthecoretechnicalprogram,ECMLandPKDDhad6invitedspeakers, 10 workshops, 8 tutorials and a Knowledge Discovery Challenge.

✦ Table of Contents

Frontmatter
Invited Talks
Data Analysis in the Life Sciences --- Sparking Ideas ---
Machine Learning for Natural Language Processing (and Vice Versa?)
Statistical Relational Learning: An Inductive Logic Programming Perspective
Recent Advances in Mining Time Series Data
Focus the Mining Beacon: Lessons and Challenges from the World of E-Commerce
Data Streams and Data Synopses for Massive Data Sets (Invited Talk)
Long Papers
Clustering and Metaclustering with Nonnegative Matrix Decompositions
A SAT-Based Version Space Algorithm for Acquiring Constraint Satisfaction Problems
Estimation of Mixture Models Using Co-EM
Nonrigid Embeddings for Dimensionality Reduction
Multi-view Discriminative Sequential Learning
Robust Bayesian Linear Classifier Ensembles
An Integrated Approach to Learning Bayesian Networks of Rules
Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam
Rotational Prior Knowledge for SVMs
On the LearnAbility of Abstraction Theories from Observations for Relational Learning
Beware the Null Hypothesis: Critical Value Tables for Evaluating Classifiers
Kernel Basis Pursuit
Hybrid Algorithms with Instance-Based Classification
Learning and Classifying Under Hard Budgets
Training Support Vector Machines with Multiple Equality Constraints
A Model Based Method for Automatic Facial Expression Recognition
Margin-Sparsity Trade-Off for the Set Covering Machine
Learning from Positive and Unlabeled Examples with Different Data Distributions
Towards Finite-Sample Convergence of Direct Reinforcement Learning
Infinite Ensemble Learning with Support Vector Machines
A Kernel Between Unordered Sets of Data: The Gaussian Mixture Approach
Active Learning for Probability Estimation Using Jensen-Shannon Divergence
Natural Actor-Critic
Inducing Head-Driven PCFGs with Latent Heads: Refining a Tree-Bank Grammar for Parsing
Learning (k,l)-Contextual Tree Languages for Information Extraction
Neural Fitted Q Iteration -- First Experiences with a Data Efficient Neural Reinforcement Learning Method
MCMC Learning of Bayesian Network Models by Markov Blanket Decomposition
On Discriminative Joint Density Modeling
Model-Based Online Learning of POMDPs
Simple Test Strategies for Cost-Sensitive Decision Trees
$\mathcal{U}$-Likelihood and $\mathcal{U}$-Updating Algorithms: Statistical Inference in Latent Variable Models
An Optimal Best-First Search Algorithm for Solving Infinite Horizon DEC-POMDPs
Ensemble Learning with Supervised Kernels
Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another
A Distance-Based Approach for Action Recommendation
Multi-armed Bandit Algorithms and Empirical Evaluation
Annealed Discriminant Analysis
Network Game and Boosting
Model Selection in Omnivariate Decision Trees
Bayesian Network Learning with Abstraction Hierarchies and Context-Specific Independence
Short Papers
Learning to Complete Sentences
The Huller: A Simple and Efficient Online SVM
Inducing Hidden Markov Models to Model Long-Term Dependencies
A Similar Fragments Merging Approach to Learn Automata on Proteins
Nonnegative Lagrangian Relaxation of {\itshape K}-Means and Spectral Clustering
Severe Class Imbalance: Why Better Algorithms Aren't the Answer
Approximation Algorithms for Minimizing Empirical Error by Axis-Parallel Hyperplanes
A Comparison of Approaches for Learning Probability Trees
Counting Positives Accurately Despite Inaccurate Classification
Optimal Stopping and Constraints for Diffusion Models of Signals with Discontinuities
An Evolutionary Function Approximation Approach to Compute Prediction in XCSF
Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes
Active Learning in Partially Observable Markov Decision Processes
Machine Learning of Plan Robustness Knowledge About Instances
Two Contributions of Constraint Programming to Machine Learning
A Clustering Model Based on Matrix Approximation with Applications to Cluster System Log Files
Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford's Law Distributions
Efficient Case Based Feature Construction
Fitting the Smallest Enclosing Bregman Ball
Similarity-Based Alignment and Generalization
Fast Non-negative Dimensionality Reduction for Protein Fold Recognition
Mode Directed Path Finding
Classification with Maximum Entropy Modeling of Predictive Association Rules
Classification of Ordinal Data Using Neural Networks
Independent Subspace Analysis on Innovations
On Applying Tabling to Inductive Logic Programming
Learning Models of Relational Stochastic Processes
Error-Sensitive Grading for Model Combination
Strategy Learning for Reasoning Agents
Combining Bias and Variance Reduction Techniques for Regression Trees
Analysis of Generic Perceptron-Like Large Margin Classifiers
Multimodal Function Optimizing by a New Hybrid Nonlinear Simplex Search and Particle Swarm Algorithm
Backmatter