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Machine Learning and Knowledge Discovery in Databases

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Table of Contents

  1. Altmetric Badge
    Book Overview
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    Chapter 1 Industrializing Data Mining, Challenges and Perspectives
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    Chapter 2 From Microscopy Images to Models of Cellular Processes
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    Chapter 3 Data Clustering: 50 Years Beyond K-means
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    Chapter 4 Learning Language from Its Perceptual Context
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    Chapter 5 The Role of Hierarchies in Exploratory Data Mining
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    Chapter 6 Rollout Sampling Approximate Policy Iteration
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    Chapter 7 New Closed-Form Bounds on the Partition Function
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    Chapter 8 Large Margin vs. Large Volume in Transductive Learning
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    Chapter 9 Incremental Exemplar Learning Schemes for Classification on Embedded Devices
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    Chapter 10 A Collaborative Filtering Framework Based on Both Local User Similarity and Global User Similarity
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    Chapter 11 A Critical Analysis of Variants of the AUC
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    Chapter 12 Improving Maximum Margin Matrix Factorization
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    Chapter 13 Finding Reliable Subgraphs from Large Probabilistic Graphs
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    Chapter 14 A Space Efficient Solution to the Frequent String Mining Problem for Many Databases
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    Chapter 15 The Boolean Column and Column-Row Matrix Decompositions
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    Chapter 16 SkyGraph: An Algorithm for Important Subgraph Discovery in Relational Graphs
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    Chapter 17 Mining Conjunctive Sequential Patterns
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    Chapter 18 Adequate Condensed Representations of Patterns
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    Chapter 19 Two Heads Better Than One: Pattern Discovery in Time-Evolving Multi-aspect Data
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    Chapter 20 TOPTMH : Topology Predictor for Transmembrane α -Helices
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    Chapter 21 Learning to Predict One or More Ranks in Ordinal Regression Tasks
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    Chapter 22 Cascade RSVM in Peer-to-Peer Networks
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    Chapter 23 An Algorithm for Transfer Learning in a Heterogeneous Environment
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    Chapter 24 Minimum-Size Bases of Association Rules
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    Chapter 25 Combining Classifiers through Triplet-Based Belief Functions
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    Chapter 26 An Improved Multi-task Learning Approach with Applications in Medical Diagnosis
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    Chapter 27 Semi-supervised Laplacian Regularization of Kernel Canonical Correlation Analysis
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    Chapter 28 Sequence Labelling SVMs Trained in One Pass
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    Chapter 29 Semi-supervised Classification from Discriminative Random Walks
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    Chapter 30 Learning Bidirectional Similarity for Collaborative Filtering
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    Chapter 31 Bootstrapping Information Extraction from Semi-structured Web Pages
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    Chapter 32 Online Multiagent Learning against Memory Bounded Adversaries
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    Chapter 33 Scalable Feature Selection for Multi-class Problems
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    Chapter 34 Learning Decision Trees for Unbalanced Data
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    Chapter 35 Credal Model Averaging: An Extension of Bayesian Model Averaging to Imprecise Probabilities
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    Chapter 36 A Fast Method for Training Linear SVM in the Primal
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    Chapter 37 On the Equivalence of the SMO and MDM Algorithms for SVM Training
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    Chapter 38 Nearest Neighbour Classification with Monotonicity Constraints
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    Chapter 39 Modeling Transfer Relationships Between Learning Tasks for Improved Inductive Transfer
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    Chapter 40 Mining Edge-Weighted Call Graphs to Localise Software Bugs
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    Chapter 41 Hierarchical Distance-Based Conceptual Clustering
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    Chapter 42 Mining Frequent Connected Subgraphs Reducing the Number of Candidates
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    Chapter 43 Unsupervised Riemannian Clustering of Probability Density Functions
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    Chapter 44 Online Manifold Regularization: A New Learning Setting and Empirical Study
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    Chapter 45 A Fast Algorithm to Find Overlapping Communities in Networks
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    Chapter 46 A Case Study in Sequential Pattern Mining for IT-Operational Risk
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    Chapter 47 Tight Optimistic Estimates for Fast Subgroup Discovery
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    Chapter 48 Watch, Listen & Learn: Co-training on Captioned Images and Videos
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    Chapter 49 Parameter Learning in Probabilistic Databases: A Least Squares Approach
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    Chapter 50 Improving k-Nearest Neighbour Classification with Distance Functions Based on Receiver Operating Characteristics
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    Chapter 51 One-Class Classification by Combining Density and Class Probability Estimation
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    Chapter 52 Efficient Frequent Connected Subgraph Mining in Graphs of Bounded Treewidth
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    Chapter 53 Proper Model Selection with Significance Test
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    Chapter 54 A Projection-Based Framework for Classifier Performance Evaluation
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    Chapter 55 Distortion-Free Nonlinear Dimensionality Reduction
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    Chapter 56 Learning with L q  
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    Chapter 57 Catenary Support Vector Machines
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    Chapter 58 Exact and Approximate Inference for Annotating Graphs with Structural SVMs
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    Chapter 59 Extracting Semantic Networks from Text Via Relational Clustering
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    Chapter 60 Ranking the Uniformity of Interval Pairs
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    Chapter 61 Multiagent Reinforcement Learning for Urban Traffic Control Using Coordination Graphs
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    Chapter 62 StreamKrimp : Detecting Change in Data Streams
Attention for Chapter 6: Rollout Sampling Approximate Policy Iteration
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Chapter title
Rollout Sampling Approximate Policy Iteration
Chapter number 6
Book title
Machine Learning and Knowledge Discovery in Databases
Published by
Springer, Berlin, Heidelberg, September 2008
DOI 10.1007/978-3-540-87479-9_6
Book ISBNs
978-3-54-087478-2, 978-3-54-087479-9
Authors

Christos Dimitrakakis, Michail G. Lagoudakis