Data analysis, machine learning and applications : proceedings of the 31st Annual Conference of the Gesellschaft fü̈r Klassifikation e.V., Albert-Ludwigs-Universität Freiburg, March 7-9, 2007

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
Author: Christine Preisach, Professor Dr. Hans Burkhardt, Professor Dr. Lars Schmidt-Thieme, Professor Dr. Reinhold Decker
Published by Springer Berlin Heidelberg
ISBN: 978-3-540-78239-1
DOI: 10.1007/978-3-540-78246-9

Table of Contents:

• Distance-Based Kernels for Real-Valued Data
• Fast Support Vector Machine Classification of Very Large Datasets
• Fusion of Multiple Statistical Classifiers
• Calibrating Margin-Based Classifier Scores into Polychotomous Probabilities
• Classification with Invariant Distance Substitution Kernels
• Applying the Kohonen Self-Organizing Map Networks to Select Variables
• Computer Assisted Classification of Brain Tumors
• Model Selection in Mixture Regression Analysis–A Monte Carlo Simulation Study
• Comparison of Local Classification Methods
• Incorporating Domain Specific Information into Gaia Source Classification
• Identification of Noisy Variables for Nonmetric and Symbolic Data in Cluster Analysis
• Families of Dendrograms
• Mixture Models in Forward Search Methods for Outlier Detection
• On Multiple Imputation Through Finite Gaussian Mixture Models
• Mixture Model Based Group Inference in Fused Genotype and Phenotype Data
• The Noise Component in Model-based Cluster Analysis
• An Artificial Life Approach for Semi-supervised Learning
• Hard and Soft Euclidean Consensus Partitions
• Rationale Models for Conceptual Modeling
• Measures of Dispersion and Cluster-Trees for Categorical Data

Includes bibliographical references and indexes