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Machine Learning: ECML 2000 [electronic resource] :11th European Conference on Machine Learning Barcelona, Catalonia, Spain, May 31 – June 2, 2000 Proceedings /

Contributor(s): López de Mántaras, Ramon [editor.] | Plaza, Enric [editor.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Lecture Notes in Computer Science, Lecture Notes in Artificial Intelligence: 1810Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg, 2000.Description: XII, 472 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783540451648.Subject(s): Computer science | Algorithms | Database management | Artificial intelligence | Computer Science | Database Management | Artificial Intelligence (incl. Robotics) | Algorithm Analysis and Problem ComplexityOnline resources: Click here to access online
Contents:
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.
In: Springer eBooksSummary: The biennial European Conference on Machine Learning (ECML) series is intended to provide an international forum for the discussion of the latest high quality research results in machine learning and is the major European scienti?c event in the ?eld. The eleventh conference (ECML 2000) held in Barcelona, Catalonia, Spain from May 31 to June 2, 2000, has continued this tradition by attracting high quality papers from around the world. Scientists from 21 countries submitted 100 papers to ECML 2000, from which 20 were selected for long oral presentations and 23 for short oral presentations. This selection was based on the recommendations of at least two reviewers for each submitted paper. It is worth noticing that the number of papers reporting applications of machine learning has increased in comparison to past ECML conferences. We believe this fact shows the growing maturity of the ?eld. This volume contains the 43 accepted papers as well as the invited talks by Katharina Morik from the University of Dortmund and Pedro Domingos from the University of Washington at Seattle. In addition, three workshops were jointly organized by ECML 2000 and the European Network of Excellence - net: “Dealing with Structured Data in Machine Learning and Statistics W- stites”, “Machine Learning in the New Information Age” , and “Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Com- nation”.
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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.

The biennial European Conference on Machine Learning (ECML) series is intended to provide an international forum for the discussion of the latest high quality research results in machine learning and is the major European scienti?c event in the ?eld. The eleventh conference (ECML 2000) held in Barcelona, Catalonia, Spain from May 31 to June 2, 2000, has continued this tradition by attracting high quality papers from around the world. Scientists from 21 countries submitted 100 papers to ECML 2000, from which 20 were selected for long oral presentations and 23 for short oral presentations. This selection was based on the recommendations of at least two reviewers for each submitted paper. It is worth noticing that the number of papers reporting applications of machine learning has increased in comparison to past ECML conferences. We believe this fact shows the growing maturity of the ?eld. This volume contains the 43 accepted papers as well as the invited talks by Katharina Morik from the University of Dortmund and Pedro Domingos from the University of Washington at Seattle. In addition, three workshops were jointly organized by ECML 2000 and the European Network of Excellence - net: “Dealing with Structured Data in Machine Learning and Statistics W- stites”, “Machine Learning in the New Information Age” , and “Meta-Learning: Building Automatic Advice Strategies for Model Selection and Method Com- nation”.

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