Machine Learning: ECML 2000

11th European Conference on Machine Learning Barcelona, Catalonia, Spain May, 31 - June 2, 2000 Proceedings

AvRamon Lopez de Mantaras,Enric Plaza

Häftad, Engelska, 2000

Del i serien Lecture Notes in Computer Science

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Beskrivning

This book constitutes the refereed proceedings of the 11th European Conference on Machine Learning, ECML 2000, held in Barcelona, Catalonia, Spain, in May/June 2000.The 20 long papers and 23 short papers presented together with 2 invited contributions were carefully reviewed and selected from 100 submissions. All current issues in machine learning as well as advanced applications in various areas are addressed.

Produktinformation

Utgivningsdatum:2000-05-17
Mått:155 x 235 x 27 mm
Vikt:727 g
Format:Häftad
Språk:Engelska
Serie:Lecture Notes in Computer Science
Antal sidor:472
Upplaga:2000
Förlag:Springer-Verlag Berlin and Heidelberg GmbH & Co. KG
ISBN:9783540676027

Utforska kategorier

Databaser inom Data och IT

Innehållsförteckning

Invited Papers.- Beyond Occam’s Razor: Process-Oriented Evaluation.- The Representation Race — Preprocessing for Handling Time Phenomena.- Contributed Papers.- Short-Term Profiling for a Case-Based Reasoning Recommendation System.- K-SVCR. A Multi-class Support Vector Machine.- Learning Trading Rules with Inductive Logic Programming.- Improving Knowledge Discovery Using Domain Knowledge in Unsupervised Learning.- Exploiting Classifier Combination for Early Melanoma Diagnosis Support.- A Comparison of Ranking Methods for Classification Algorithm Selection.- Hidden Markov Models with Patterns and Their Application to Integrated Circuit Testing.- Comparing Complete and Partial Classification for Identifying Latently Dissatisfied Customers.- Wrapper Generation via Grammar Induction.- Diversity versus Quality in Classification Ensembles Based on Feature Selection.- Minimax TD-Learning with Neural Nets in a Markov Game.- Boosting Applied to Word Sense Disambiguation.- A Multiple Model Cost-Sensitive Approach for Intrusion Detection.- Value Miner: A Data Mining Environment for the Calculation of the Customer Lifetime Value with Application to the Automotive Industry.- Investigation and Reduction of Discretization Variance in Decision Tree Induction.- Asymmetric Co-evolution for Imperfect-Information Zero-Sum Games.- A Machine Learning Approach to Workflow Management.- The Utilization of Context Signals in the Analysis of ABR Potentials by Application of Neural Networks.- Complexity Approximation Principle and Rissanen’s Approach to Real-Valued Parameters.- Handling Continuous-Valued Attributes in Decision Tree with Neural Network Modeling.- Learning Context-Free Grammars with a Simplicity Bias.- Partially Supervised Text Classification: Combining Labeled and Unlabeled Documents Using an EM-like Scheme.- Toward an Explanatory Similarity Measure for Nearest-Neighbor Classification.- Relative Unsupervised Discretization for Regression Problems.- Metric-Based Inductive Learning Using Semantic Height Functions.- Error Analysis of Automatic Speech Recognition Using Principal Direction Divisive Partitioning.- A Study on the Performance of Large Bayes Classifier.- Dynamic Discretization of Continuous Values from Time Series.- Using a Symbolic Machine Learning Tool to Refine Lexico-syntactic Patterns.- Measuring Performance when Positives Are Rare: Relative Advantage versus Predictive Accuracy — A Biological Case-Study.- Mining TCP/IP Traffic for Network Intrusion Detection by Using a Distributed Genetic Algorithm.- Learning Patterns of Behavior by Observing System Events.- Dimensionality Reduction through Sub-space Mapping for Nearest Neighbour Algorithms.- Nonparametric Regularization of Decision Trees.- An Efficient and Effective Procedure for Updating a Competence Model for Case-Based Reasoners.- Layered Learning.- Problem Decomposition for Behavioural Cloning.- Dynamic Feature Selection in Incremental Hierarchical Clustering.- On the Boosting Pruning Problem.- An Empirical Study of MetaCost Using Boosting Algorithms.- Clustered Partial Linear Regression.- Knowledge Discovery from Very Large Databases Using Frequent Concept Lattices.- Some Improvements on Event-Sequence Temporal Region Methods.