Machine Learning: ECML 2006

Table of Contents

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   Book Overview
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   Chapter 1 On Temporal Evolution in Data Streams
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   Chapter 2 The Future of CiteSeer: CiteSeer x
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   Chapter 3 Learning to Have Fun
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   Chapter 4 Winning the DARPA Grand Challenge
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   Chapter 5 Challenges of Urban Sensing
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   Chapter 6 Learning in One-Shot Strategic Form Games
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   Chapter 7 A Selective Sampling Strategy for Label Ranking
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   Chapter 8 Combinatorial Markov Random Fields
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    Chapter 9 Learning Stochastic Tree Edit Distance
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    Chapter 10 Pertinent Background Knowledge for Learning Protein Grammars
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    Chapter 11 Improving Bayesian Network Structure Search with Random Variable Aggregation Hierarchies
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    Chapter 12 Sequence Discrimination Using Phase-Type Distributions
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    Chapter 13 Languages as Hyperplanes: Grammatical Inference with String Kernels
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    Chapter 14 Toward Robust Real-World Inference: A New Perspective on Explanation-Based Learning
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    Chapter 15 Fisher Kernels for Relational Data
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    Chapter 16 Evaluating Misclassifications in Imbalanced Data
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    Chapter 17 Improving Control-Knowledge Acquisition for Planning by Active Learning
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    Chapter 18 PAC-Learning of Markov Models with Hidden State
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    Chapter 19 A Discriminative Approach for the Retrieval of Images from Text Queries
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    Chapter 20 TildeCRF: Conditional Random Fields for Logical Sequences
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    Chapter 21 Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data
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    Chapter 22 Bayesian Learning of Markov Network Structure
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    Chapter 23 Approximate Policy Iteration for Closed-Loop Learning of Visual Tasks
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    Chapter 24 Task-Driven Discretization of the Joint Space of Visual Percepts and Continuous Actions
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    Chapter 25 EM Algorithm for Symmetric Causal Independence Models
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    Chapter 26 Deconvolutive Clustering of Markov States
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    Chapter 27 Patching Approximate Solutions in Reinforcement Learning
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    Chapter 28 Machine Learning: ECML 2006
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    Chapter 29 Bandit Based Monte-Carlo Planning
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    Chapter 30 Bayesian Learning with Mixtures of Trees
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    Chapter 31 Transductive Gaussian Process Regression with Automatic Model Selection
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    Chapter 32 Efficient Convolution Kernels for Dependency and Constituent Syntactic Trees
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    Chapter 33 Why Is Rule Learning Optimistic and How to Correct It
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    Chapter 34 Automatically Evolving Rule Induction Algorithms
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    Chapter 35 Bayesian Active Learning for Sensitivity Analysis
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    Chapter 36 Mixtures of Kikuchi Approximations
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    Chapter 37 Boosting in PN Spaces
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    Chapter 38 Prioritizing Point-Based POMDP Solvers
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    Chapter 39 Graph Based Semi-supervised Learning with Sharper Edges
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    Chapter 40 Margin-Based Active Learning for Structured Output Spaces
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    Chapter 41 Skill Acquisition Via Transfer Learning and Advice Taking
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    Chapter 42 Constant Rate Approximate Maximum Margin Algorithms
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    Chapter 43 Batch Classification with Applications in Computer Aided Diagnosis
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    Chapter 44 Improving the Ranking Performance of Decision Trees
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    Chapter 45 Multiple-Instance Learning Via Random Walk
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    Chapter 46 Localized Alternative Cluster Ensembles for Collaborative Structuring
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    Chapter 47 Distributional Features for Text Categorization
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    Chapter 48 Subspace Metric Ensembles for Semi-supervised Clustering of High Dimensional Data
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    Chapter 49 An Adaptive Kernel Method for Semi-supervised Clustering
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    Chapter 50 To Select or To Weigh: A Comparative Study of Model Selection and Model Weighing for SPODE Ensembles
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    Chapter 51 Ensembles of Nearest Neighbor Forecasts
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    Chapter 52 Learning Process Models with Missing Data
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    Chapter 53 Case-Based Label Ranking
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    Chapter 54 Cascade Evaluation of Clustering Algorithms
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    Chapter 55 Making Good Probability Estimates for Regression
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    Chapter 56 Fast Spectral Clustering of Data Using Sequential Matrix Compression
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    Chapter 57 An Information-Theoretic Framework for High-Order Co-clustering of Heterogeneous Objects
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    Chapter 58 Machine Learning: ECML 2006
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    Chapter 59 A Kernel-Based Approach to Estimating Phase Shifts Between Irregularly Sampled Time Series: An Application to Gravitational Lenses
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    Chapter 60 Cost-Sensitive Decision Tree Learning for Forensic Classification
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    Chapter 61 Machine Learning: ECML 2006
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    Chapter 62 Right of Inference: Nearest Rectangle Learning Revisited
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    Chapter 63 Reinforcement Learning for MDPs with Constraints
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    Chapter 64 Efficient Non-linear Control Through Neuroevolution
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    Chapter 65 Machine Learning: ECML 2006
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    Chapter 66 On Testing the Missing at Random Assumption
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    Chapter 67 B-Matching for Spectral Clustering
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    Chapter 68 Multi-class Ensemble-Based Active Learning
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    Chapter 69 Active Learning with Irrelevant Examples
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    Chapter 70 Classification with Support Hyperplanes
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    Chapter 71 (Agnostic) PAC Learning Concepts in Higher-Order Logic
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    Chapter 72 Evaluating Feature Selection for SVMs in High Dimensions
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    Chapter 73 Revisiting Fisher Kernels for Document Similarities
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    Chapter 74 Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery
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    Chapter 75 Robust Probabilistic Calibration
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    Chapter 76 Missing Data in Kernel PCA
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    Chapter 77 Exploiting Extremely Rare Features in Text Categorization
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    Chapter 78 Efficient Large Scale Linear Programming Support Vector Machines
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    Chapter 79 An Efficient Approximation to Lookahead in Relational Learners
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    Chapter 80 Improvement of Systems Management Policies Using Hybrid Reinforcement Learning
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    Chapter 81 Diversified SVM Ensembles for Large Data Sets
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    Chapter 82 Dynamic Integration with Random Forests
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    Chapter 83 Bagging Using Statistical Queries
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    Chapter 84 Guiding the Search in the NO Region of the Phase Transition Problem with a Partial Subsumption Test
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    Chapter 85 Spline Embedding for Nonlinear Dimensionality Reduction
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    Chapter 86 Cost-Sensitive Learning of SVM for Ranking
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    Chapter 87 Variational Bayesian Dirichlet-Multinomial Allocation for Exponential Family Mixtures