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Machine Learning: ECML 2002

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Cover of 'Machine Learning: ECML 2002'

Table of Contents

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    Book Overview
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    Chapter 1 Convergent Gradient Ascent in General-Sum Games
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    Chapter 2 Revising Engineering Models: Combining Computational Discovery with Knowledge
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    Chapter 3 Variational Extensions to EM and Multinomial PCA
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    Chapter 4 Learning and Inference for Clause Identification
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    Chapter 5 An Empirical Study of Encoding Schemes and Search Strategies in Discovering Causal Networks
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    Chapter 6 Variance Optimized Bagging
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    Chapter 7 How to Make AdaBoost.M1 Work for Weak Base Classifiers by Changing Only One Line of the Code
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    Chapter 8 Sparse Online Greedy Support Vector Regression
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    Chapter 9 Pairwise Classification as an Ensemble Technique
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    Chapter 10 RIONA: A Classifier Combining Rule Induction and k-NN Method with Automated Selection of Optimal Neighbourhood
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    Chapter 11 Using Hard Classifiers to Estimate Conditional Class Probabilities
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    Chapter 12 Evidence that Incremental Delta-Bar-Delta Is an Attribute-Efficient Linear Learner
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    Chapter 13 Scaling Boosting by Margin-Based Inclusion of Features and Relations
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    Chapter 14 Multiclass Alternating Decision Trees
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    Chapter 15 Possibilistic Induction in Decision-Tree Learning
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    Chapter 16 Improved Smoothing for Probabilistic Suffix Trees Seen as Variable Order Markov Chains
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    Chapter 17 Collaborative Learning of Term-Based Concepts for Automatic Query Expansion
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    Chapter 18 Learning to Play a Highly Complex Game from Human Expert Games
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    Chapter 19 Reliable Classifications with Machine Learning
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    Chapter 20 Robustness Analyses of Instance-Based Collaborative Recommendation
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    Chapter 21 i Boost: Boosting Using an i nstance-Based Exponential Weighting Scheme
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    Chapter 22 Towards a Simple Clustering Criterion Based on Minimum Length Encoding
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    Chapter 23 Class Probability Estimation and Cost-Sensitive Classification Decisions
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    Chapter 24 On-Line Support Vector Machine Regression
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    Chapter 25 Q-Cut—Dynamic Discovery of Sub-goals in Reinforcement Learning
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    Chapter 26 A Multistrategy Approach to the Classification of Phases in Business Cycles
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    Chapter 27 A Robust Boosting Algorithm
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    Chapter 28 Case Exchange Strategies in Multiagent Learning
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    Chapter 29 Inductive Confidence Machines for Regression
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    Chapter 30 Macro-Operators in Multirelational Learning: A Search-Space Reduction Technique
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    Chapter 31 Propagation of Q-values in Tabular TD(λ)
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    Chapter 32 Transductive Confidence Machines for Pattern Recognition
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    Chapter 33 Characterizing Markov Decision Processes
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    Chapter 34 Phase Transitions and Stochastic Local Search in k-Term DNF Learning
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    Chapter 35 Discriminative Clustering: Optimal Contingency Tables by Learning Metrics
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    Chapter 36 Boosting Density Function Estimators
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    Chapter 37 Ranking with Predictive Clustering Trees
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    Chapter 38 Support Vector Machines for Polycategorical Classification
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    Chapter 39 Learning Classification with Both Labeled and Unlabeled Data
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    Chapter 40 An Information Geometric Perspective on Active Learning
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    Chapter 41 Stacking with an Extended Set of Meta-level Attributes and MLR
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    Chapter 42 Finding Hidden Factors Using Independent Component Analysis
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    Chapter 43 Reasoning with Classifiers
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    Chapter 44 A Kernel Approach for Learning from almost Orthogonal Patterns
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    Chapter 45 Learning with Mixture Models: Concepts and Applications
Attention for Chapter 6: Variance Optimized Bagging
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Chapter title
Variance Optimized Bagging
Chapter number 6
Book title
Machine Learning: ECML 2002
Published by
Springer, Berlin, Heidelberg, August 2002
DOI 10.1007/3-540-36755-1_6
Book ISBNs
978-3-54-044036-9, 978-3-54-036755-0
Authors

Philip Derbeko, Ran El-Yaniv, Ron Meir, Derbeko, Philip, El-Yaniv, Ran, Meir, Ron

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 25 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Germany 1 4%
India 1 4%
Russia 1 4%
Spain 1 4%
United States 1 4%
Poland 1 4%
Unknown 19 76%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 24%
Professor > Associate Professor 4 16%
Student > Bachelor 3 12%
Student > Master 3 12%
Professor 2 8%
Other 3 12%
Unknown 4 16%
Readers by discipline Count As %
Computer Science 8 32%
Engineering 4 16%
Economics, Econometrics and Finance 4 16%
Business, Management and Accounting 2 8%
Mathematics 1 4%
Other 0 0%
Unknown 6 24%