Machine Learning: ECML 2005

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.- -Likelihood and -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 EfficientOnline SVM.- Inducing Hidden Markov Models to Model Long-Term Dependencies.- A Similar Fragments Merging Approach to Learn Automata on Proteins.- Nonnegative Lagrangian Relaxation of 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.