Machine Learning: ECML 2006

• Invited Talks.- On Temporal Evolution in Data Streams.- The Future of CiteSeer: CiteSeerx.- Learning to Have Fun.- Winning the DARPA Grand Challenge.- Challenges of Urban Sensing.- Long Papers.- Learning in One-Shot Strategic Form Games.- A Selective Sampling Strategy for Label Ranking.- Combinatorial Markov Random Fields.- Learning Stochastic Tree Edit Distance.- Pertinent Background Knowledge for Learning Protein Grammars.- Improving Bayesian Network Structure Search with Random Variable Aggregation Hierarchies.- Sequence Discrimination Using Phase-Type Distributions.- Languages as Hyperplanes: Grammatical Inference with String Kernels.- Toward Robust Real-World Inference: A New Perspective on Explanation-Based Learning.- Fisher Kernels for Relational Data.- Evaluating Misclassifications in Imbalanced Data.- Improving Control-Knowledge Acquisition for Planning by Active Learning.- PAC-Learning of Markov Models with Hidden State.- A Discriminative Approach for the Retrieval of Imagesfrom Text Queries.- TildeCRF: Conditional Random Fields for Logical Sequences.- Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data.- Bayesian Learning of Markov Network Structure.- Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks.- Task-Driven Discretization of the Joint Space of Visual Percepts and Continuous Actions.- EM Algorithm for Symmetric Causal Independence Models.- Deconvolutive Clustering of Markov States.- Patching Approximate Solutions in Reinforcement Learning.- Fast Variational Inference for Gaussian Process Models Through KL-Correction.- Bandit Based Monte-Carlo Planning.- Bayesian Learning with Mixtures of Trees.- Transductive Gaussian Process Regression with Automatic Model Selection.- Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees.- Why Is Rule Learning Optimistic and How to Correct It.- Automatically Evolving Rule Induction Algorithms.- Bayesian Active Learning for Sensitivity Analysis.- Mixtures of Kikuchi Approximations.- Boosting in PN Spaces.- Prioritizing Point-Based POMDP Solvers.- Graph Based Semi-supervised Learning with Sharper Edges.- Margin-Based Active Learning for Structured Output Spaces.- Skill Acquisition Via Transfer Learning and Advice Taking.- Constant Rate Approximate Maximum Margin Algorithms.- Batch Classification with Applications in Computer Aided Diagnosis.- Improving the Ranking Performance of Decision Trees.- Multiple-Instance Learning Via Random Walk.- Localized Alternative Cluster Ensembles for Collaborative Structuring.- Distributional Features for Text Categorization.- Subspace Metric Ensembles for Semi-supervised Clustering of High Dimensional Data.- An Adaptive Kernel Method for Semi-supervised Clustering.- To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles.- Ensembles of Nearest Neighbor Forecasts.- Short Papers.- Learning Process Models with Missing Data.- Case-Based Label Ranking.-Cascade Evaluation of Clustering Algorithms.- Making Good Probability Estimates for Regression.- Fast Spectral Clustering of Data Using Sequential Matrix Compression.- An Information-Theoretic Framework for High-Order Co-clustering of Heterogeneous Objects.- Efficient Inference in Large Conditional Random Fields.- A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses.- Cost-Sensitive Decision Tree Learning for Forensic Classification.- The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces.- Right of Inference: Nearest Rectangle Learning Revisited.- Reinforcement Learning for MDPs with Constraints.- Efficient Non-linear Control Through Neuroevolution.- Efficient Prediction-Based Validation for Document Clustering.- On Testing the Missing at Random Assumption.- B-Matching for Spectral Clustering.- Multi-class Ensemble-Based Active Learning.- Active Learning with Irrelevant Examples.-Classification with Support Hyperplanes.- (Agnostic) PAC Learning Concepts in Higher-Order Logic.- Evaluating Feature Selection for SVMs in High Dimensions.- Revisiting Fisher Kernels for Document Similarities.- Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery.- Robust Probabilistic Calibration.- Missing Data in Kernel PCA.- Exploiting Extremely Rare Features in Text Categorization.- Efficient Large Scale Linear Programming Support Vector Machines.- An Efficient Approximation to Lookahead in Relational Learners.- Improvement of Systems Management Policies Using Hybrid Reinforcement Learning.- Diversified SVM Ensembles for Large Data Sets.- Dynamic Integration with Random Forests.- Bagging Using Statistical Queries.- Guiding the Search in the NO Region of the Phase Transition Problem with a Partial Subsumption Test.- Spline Embedding for Nonlinear Dimensionality Reduction.- Cost-Sensitive Learning of SVM for Ranking.- Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures.

From the reviews: "In this book, we find many ways of representing machine learning from different fields, including active learning, algorithmic learning, case-based learning, classifier systems, clustering algorithms, decision-tree learning, inductive inference, kernel methods, knowledge discovery, multiple-instance learning, reinforcement learning, statistical learning, and support vector machines. Most of the current issues in machine learning research are discussed. ... I strongly recommend this book for all researchers interested in the very best of machine learning studies." (Agliberto Cierco, ACM Computing Reviews, Vol. 49 (5), 2008)