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Machine Learning: ECML 2001

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Cover of 'Machine Learning: ECML 2001'

Table of Contents

  1. Altmetric Badge
    Book Overview
  2. Altmetric Badge
    Chapter 1 An Axiomatic Approach to Feature Term Generalization
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    Chapter 2 Lazy Induction of Descriptions for Relational Case-Based Learning
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    Chapter 3 Estimating the Predictive Accuracy of a Classifier
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    Chapter 4 Improving the Robustness and Encoding Complexity of Behavioural Clones
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    Chapter 5 A Framework for Learning Rules from Multiple Instance Data
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    Chapter 6 Wrapping Web Information Providers by Transducer Induction
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    Chapter 7 Learning While Exploring: Bridging the Gaps in the Eligibility Traces
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    Chapter 8 A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold’em Poker
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    Chapter 9 Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner
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    Chapter 10 Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example
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    Chapter 11 Iterative Double Clustering for Unsupervised and Semi-supervised Learning
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    Chapter 12 On the Practice of Branching Program Boosting
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    Chapter 13 A Simple Approach to Ordinal Classification
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    Chapter 14 Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem
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    Chapter 15 Extraction of Recurrent Patterns from Stratified Ordered Trees
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    Chapter 16 Understanding Probabilistic Classifiers
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    Chapter 17 Efficiently Determining the Starting Sample Size for Progressive Sampling
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    Chapter 18 Using Subclasses to Improve Classification Learning
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    Chapter 19 Learning What People (Don’t) Want
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    Chapter 20 Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions
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    Chapter 21 Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences
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    Chapter 22 Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction
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    Chapter 23 Learning of Variability for Invariant Statistical Pattern Recognition
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    Chapter 24 The Evaluation of Predictive Learners: Some Theoretical and Empirical Results
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    Chapter 25 An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning
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    Chapter 26 A Mixture Approach to Novelty Detection Using Training Data with Outliers
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    Chapter 27 Applying the Bayesian Evidence Framework to ν-Support Vector Regression
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    Chapter 28 DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning
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    Chapter 29 A Language-Based Similarity Measure
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    Chapter 30 Backpropagation in Decision Trees for Regression
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    Chapter 31 Comparing the Bayes and Typicalness Frameworks
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    Chapter 32 Symbolic Discriminant Analysis for Mining Gene Expression Patterns
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    Chapter 33 Social Agents Playing a Periodical Policy
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    Chapter 34 Learning When to Collaborate among Learning Agents
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    Chapter 35 Building Committees by Clustering Models Based on Pairwise Similarity Values
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    Chapter 36 Second Order Features for Maximising Text Classification Performance
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    Chapter 37 Importance Sampling Techniques in Neural Detector Training
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    Chapter 38 Induction of Qualitative Trees
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    Chapter 39 Text Categorization Using Transductive Boosting
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    Chapter 40 Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing
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    Chapter 41 Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery
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    Chapter 42 Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL
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    Chapter 43 A Unified Framework for Evaluation Metrics in Classification Using Decision Trees
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    Chapter 44 Improving Term Extraction by System Combination Using Boosting
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    Chapter 45 Classification on Data with Biased Class Distribution
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    Chapter 46 Discovering Admissible Simultaneous Equation Models from Observed Data
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    Chapter 47 Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy
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    Chapter 48 Proportional k-Interval Discretization for Naive-Bayes Classifiers
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    Chapter 49 Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error
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    Chapter 50 Geometric Properties of Naive Bayes in Nominal Domains
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    Chapter 51 Support Vectors for Reinforcement Learning
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    Chapter 52 Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining
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    Chapter 53 Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining
  55. Altmetric Badge
    Chapter 54 The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery
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    Chapter 55 Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery
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Title
Machine Learning: ECML 2001
Published by
Springer Science & Business Media, August 2001
DOI 10.1007/3-540-44795-4
ISBNs
978-3-54-042536-6, 978-3-54-044795-5
Authors

Raedt, Luc de, Flach, Peter

Editors

Luc De Raedt, Peter Flach

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 6 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 6 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 2 33%
Researcher 1 17%
Student > Master 1 17%
Unknown 2 33%
Readers by discipline Count As %
Computer Science 2 33%
Agricultural and Biological Sciences 1 17%
Neuroscience 1 17%
Unknown 2 33%