Machine Learning: ECML 2005

Table of Contents

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   Book Overview
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   Chapter 1 Data Analysis in the Life Sciences — Sparking Ideas —
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   Chapter 2 Machine Learning for Natural Language Processing (and Vice Versa?)
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   Chapter 3 Statistical Relational Learning: An Inductive Logic Programming Perspective
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   Chapter 4 Recent Advances in Mining Time Series Data
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   Chapter 5 Focus the Mining Beacon: Lessons and Challenges from the World of E-Commerce
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   Chapter 6 Data Streams and Data Synopses for Massive Data Sets (Invited Talk)
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   Chapter 7 Clustering and Metaclustering with Nonnegative Matrix Decompositions
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   Chapter 8 A SAT-Based Version Space Algorithm for Acquiring Constraint Satisfaction Problems
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    Chapter 9 Estimation of Mixture Models Using Co-EM
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    Chapter 10 Nonrigid Embeddings for Dimensionality Reduction
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    Chapter 11 Multi-view Discriminative Sequential Learning
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    Chapter 12 Robust Bayesian Linear Classifier Ensembles
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    Chapter 13 An Integrated Approach to Learning Bayesian Networks of Rules
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    Chapter 14 Thwarting the Nigritude Ultramarine: Learning to Identify Link Spam
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    Chapter 15 Rotational Prior Knowledge for SVMs
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    Chapter 16 On the LearnAbility of Abstraction Theories from Observations for Relational Learning
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    Chapter 17 Beware the Null Hypothesis: Critical Value Tables for Evaluating Classifiers
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    Chapter 18 Kernel Basis Pursuit
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    Chapter 19 Hybrid Algorithms with Instance-Based Classification
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    Chapter 20 Learning and Classifying Under Hard Budgets
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    Chapter 21 Training Support Vector Machines with Multiple Equality Constraints
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    Chapter 22 A Model Based Method for Automatic Facial Expression Recognition
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    Chapter 23 Margin-Sparsity Trade-Off for the Set Covering Machine
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    Chapter 24 Learning from Positive and Unlabeled Examples with Different Data Distributions
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    Chapter 25 Towards Finite-Sample Convergence of Direct Reinforcement Learning
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    Chapter 26 Infinite Ensemble Learning with Support Vector Machines
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    Chapter 27 A Kernel Between Unordered Sets of Data: The Gaussian Mixture Approach
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    Chapter 28 Active Learning for Probability Estimation Using Jensen-Shannon Divergence
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    Chapter 29 Natural Actor-Critic
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    Chapter 30 Inducing Head-Driven PCFGs with Latent Heads: Refining a Tree-Bank Grammar for Parsing
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    Chapter 31 Learning (k,l)-Contextual Tree Languages for Information Extraction
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    Chapter 32 Neural Fitted Q Iteration – First Experiences with a Data Efficient Neural Reinforcement Learning Method
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    Chapter 33 MCMC Learning of Bayesian Network Models by Markov Blanket Decomposition
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    Chapter 34 On Discriminative Joint Density Modeling
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    Chapter 35 Model-Based Online Learning of POMDPs
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    Chapter 36 Simple Test Strategies for Cost-Sensitive Decision Trees
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    Chapter 37 $\mathcal{U}$ -Likelihood and $\mathcal{U}$ -Updating Algorithms: Statistical Inference in Latent Variable Models
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    Chapter 38 An Optimal Best-First Search Algorithm for Solving Infinite Horizon DEC-POMDPs
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    Chapter 39 Ensemble Learning with Supervised Kernels
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    Chapter 40 Using Advice to Transfer Knowledge Acquired in One Reinforcement Learning Task to Another
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    Chapter 41 A Distance-Based Approach for Action Recommendation
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    Chapter 42 Multi-armed Bandit Algorithms and Empirical Evaluation
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    Chapter 43 Annealed Discriminant Analysis
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    Chapter 44 Network Game and Boosting
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    Chapter 45 Model Selection in Omnivariate Decision Trees
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    Chapter 46 Bayesian Network Learning with Abstraction Hierarchies and Context-Specific Independence
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    Chapter 47 Learning to Complete Sentences
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    Chapter 48 The Huller: A Simple and Efficient Online SVM
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    Chapter 49 Inducing Hidden Markov Models to Model Long-Term Dependencies
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    Chapter 50 A Similar Fragments Merging Approach to Learn Automata on Proteins
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    Chapter 51 Nonnegative Lagrangian Relaxation of K-Means and Spectral Clustering
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    Chapter 52 Severe Class Imbalance: Why Better Algorithms Aren’t the Answer
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    Chapter 53 Approximation Algorithms for Minimizing Empirical Error by Axis-Parallel Hyperplanes
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    Chapter 54 A Comparison of Approaches for Learning Probability Trees
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    Chapter 55 Counting Positives Accurately Despite Inaccurate Classification
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    Chapter 56 Optimal Stopping and Constraints for Diffusion Models of Signals with Discontinuities
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    Chapter 57 An Evolutionary Function Approximation Approach to Compute Prediction in XCSF
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    Chapter 58 Using Rewards for Belief State Updates in Partially Observable Markov Decision Processes
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    Chapter 59 Active Learning in Partially Observable Markov Decision Processes
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    Chapter 60 Machine Learning of Plan Robustness Knowledge About Instances
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    Chapter 61 Two Contributions of Constraint Programming to Machine Learning
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    Chapter 62 A Clustering Model Based on Matrix Approximation with Applications to Cluster System Log Files
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    Chapter 63 Detecting Fraud in Health Insurance Data: Learning to Model Incomplete Benford’s Law Distributions
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    Chapter 64 Efficient Case Based Feature Construction
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    Chapter 65 Fitting the Smallest Enclosing Bregman Ball
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    Chapter 66 Similarity-Based Alignment and Generalization
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    Chapter 67 Fast Non-negative Dimensionality Reduction for Protein Fold Recognition
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    Chapter 68 Mode Directed Path Finding
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    Chapter 69 Classification with Maximum Entropy Modeling of Predictive Association Rules
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    Chapter 70 Classification of Ordinal Data Using Neural Networks
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    Chapter 71 Independent Subspace Analysis on Innovations
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    Chapter 72 On Applying Tabling to Inductive Logic Programming
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    Chapter 73 Learning Models of Relational Stochastic Processes
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    Chapter 74 Error-Sensitive Grading for Model Combination
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    Chapter 75 Strategy Learning for Reasoning Agents
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    Chapter 76 Combining Bias and Variance Reduction Techniques for Regression Trees
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    Chapter 77 Analysis of Generic Perceptron-Like Large Margin Classifiers
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    Chapter 78 Multimodal Function Optimizing by a New Hybrid Nonlinear Simplex Search and Particle Swarm Algorithm