Machine Learning: ECML 2001 : 12th European Conference on Machine Learning, Freiburg, Germany, September 5-7, 2001. Proceedings / edited by Luc de Raedt, Peter Flach.

Format:

Book

Contributor:

Raedt, Luc de, 1964- editor.

Flach, Peter A., editor.

SpringerLink (Online service)

Series:

Computer Science (Springer-11645)

Lecture notes in computer science. Lecture notes in artificial intelligence ; 2167.

Lecture Notes in Artificial Intelligence ; 2167

Language:

English

Subjects (All):

Artificial intelligence.

Computer programming.

Algorithms.

Logic, Symbolic and mathematical.

Artificial Intelligence.

Programming Techniques.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Local Subjects:

Artificial Intelligence.

Programming Techniques.

Algorithm Analysis and Problem Complexity.

Mathematical Logic and Formal Languages.

Physical Description:

1 online resource (XVII, 620 pages).

Edition:

First edition 2001.

Contained In:

Springer eBooks

Place of Publication:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2001.

System Details:

text file PDF

Summary:

This book constitutes the refereed proceedings of the 12th European Conference on Machine Learning, ECML 2001, held in Freiburg, Germany, in September 2001. The 50 revised full papers presented together with four invited contributions were carefully reviewed and selected from a total of 140 submissions. Among the topics covered are classifier systems, naive-Bayes classification, rule learning, decision tree-based classification, Web mining, equation discovery, inductive logic programming, text categorization, agent learning, backpropagation, reinforcement learning, sequence prediction, sequential decisions, classification learning, sampling, and semi-supervised learning.

Contents:

Regular Papers

An Axiomatic Approach to Feature Term Generalization

Lazy Induction of Descriptions for Relational Case-Based Learning

Estimating the Predictive Accuracy of a Classifier

Improving the Robustness and Encoding Complexity of Behavioural Clones

A Framework for Learning Rules from Multiple Instance Data

Wrapping Web Information Providers by Transducer Induction

Learning While Exploring: Bridging the Gaps in the Eligibility Traces

A Reinforcement Learning Algorithm Applied to Simplified Two-Player Texas Hold'em Poker

Speeding Up Relational Reinforcement Learning through the Use of an Incremental First Order Decision Tree Learner

Analysis of the Performance of AdaBoost.M2 for the Simulated Digit-Recognition-Example

Iterative Double Clustering for Unsupervised and Semi-supervised Learning

On the Practice of Branching Program Boosting

A Simple Approach to Ordinal Classification

Fitness Distance Correlation of Neural Network Error Surfaces: A Scalable, Continuous Optimization Problem

Extraction of Recurrent Patterns from Stratified Ordered Trees

Understanding Probabilistic Classifiers

Efficiently Determining the Starting Sample Size for Progressive Sampling

Using Subclasses to Improve Classification Learning

Learning What People (Don't) Want

Towards a Universal Theory of Artificial Intelligence Based on Algorithmic Probability and Sequential Decisions

Convergence and Error Bounds for Universal Prediction of Nonbinary Sequences

Consensus Decision Trees: Using Consensus Hierarchical Clustering for Data Relabelling and Reduction

Learning of Variability for Invariant Statistical Pattern Recognition

The Evaluation of Predictive Learners: Some Theoretical and Empirical Results

An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning

A Mixture Approach to Novelty Detection Using Training Data with Outliers

Applying the Bayesian Evidence Framework to ?-Support Vector Regression

DQL: A New Updating Strategy for Reinforcement Learning Based on Q-Learning

A Language-Based Similarity Measure

Backpropagation in Decision Trees for Regression

Comparing the Bayes and Typicalness Frameworks

Symbolic Discriminant Analysis for Mining Gene Expression Patterns

Social Agents Playing a Periodical Policy

Learning When to Collaborate among Learning Agents

Building Committees by Clustering Models Based on Pairwise Similarity Values

Second Order Features for Maximising Text Classification Performance

Importance Sampling Techniques in Neural Detector Training

Induction of Qualitative Trees

Text Categorization Using Transductive Boosting

Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing

Using Domain Knowledge on Population Dynamics Modeling for Equation Discovery

Mining the Web for Synonyms: PMI-IR versus LSA on TOEFL

A Unified Framework for Evaluation Metrics in Classification Using Decision Trees

Improving Term Extraction by System Combination Using Boosting

Classification on Data with Biased Class Distribution

Discovering Admissible Simultaneous Equation Models from Observed Data

Discovering Strong Principles of Expressive Music Performance with the PLCG Rule Learning Strategy

Proportional k-Interval Discretization for Naive-Bayes Classifiers

Using Diversity in Preparing Ensembles of Classifiers Based on Different Feature Subsets to Minimize Generalization Error

Geometric Properties of Naive Bayes in Nominal Domains

Invited Papers

Support Vectors for Reinforcement Learning

Combining Discrete Algorithmic and Probabilistic Approaches in Data Mining

Statistification or Mystification? The Need for Statistical Thought in Visual Data Mining

The Musical Expression Project: A Challenge for Machine Learning and Knowledge Discovery

Scalability, Search, and Sampling: From Smart Algorithms to Active Discovery.

Other Format:

Printed edition:

ISBN:

978-3-540-44795-5

9783540447955

Access Restriction:

Restricted for use by site license.