Data Analysis, Machine Learning and Applications

Proceedings of the 31st Annual Conference of the Gesellschaft für Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7–9, 2007

ISBN: 978-3-540-78239-1 (Print) 978-3-540-78246-9 (Online)

Table of contents (83 chapters)

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  1. Front Matter

    Pages I-XVI

  2. Classification

    1. Chapter

      Pages 3-10

      Distance-Based Kernels for Real-Valued Data

    2. Chapter

      Pages 11-18

      Fast Support Vector Machine Classification of Very Large Datasets

    3. Chapter

      Pages 19-27

      Fusion of Multiple Statistical Classifiers

    4. Chapter

      Pages 29-36

      Calibrating Margin-Based Classifier Scores into Polychotomous Probabilities

    5. Chapter

      Pages 37-44

      Classification with Invariant Distance Substitution Kernels

    6. Chapter

      Pages 45-54

      Applying the Kohonen Self-Organizing Map Networks to Select Variables

    7. Chapter

      Pages 55-60

      Computer Assisted Classification of Brain Tumors

    8. Chapter

      Pages 61-68

      Model Selection in Mixture Regression Analysis–A Monte Carlo Simulation Study

    9. Chapter

      Pages 69-76

      Comparison of Local Classification Methods

    10. Chapter

      Pages 77-83

      Incorporating Domain Specific Information into Gaia Source Classification

    11. Chapter

      Pages 85-92

      Identification of Noisy Variables for Nonmetric and Symbolic Data in Cluster Analysis

  3. Clustering

    1. Chapter

      Pages 95-102

      Families of Dendrograms

    2. Chapter

      Pages 103-110

      Mixture Models in Forward Search Methods for Outlier Detection

    3. Chapter

      Pages 111-118

      On Multiple Imputation Through Finite Gaussian Mixture Models

    4. Chapter

      Pages 119-126

      Mixture Model Based Group Inference in Fused Genotype and Phenotype Data

    5. Chapter

      Pages 127-138

      The Noise Component in Model-based Cluster Analysis

    6. Chapter

      Pages 139-146

      An Artificial Life Approach for Semi-supervised Learning

    7. Chapter

      Pages 147-154

      Hard and Soft Euclidean Consensus Partitions

    8. Chapter

      Pages 155-162

      Rationale Models for Conceptual Modeling

    9. Chapter

      Pages 163-170

      Measures of Dispersion and Cluster-Trees for Categorical Data

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