Machine Learning and Data Mining in Pattern Recognition

8th International Conference, MLDM 2012, Berlin, Germany, July 13-20, 2012. Proceedings

Editors:

ISBN: 978-3-642-31536-7 (Print) 978-3-642-31537-4 (Online)

Table of contents (51 chapters)

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  1. Front Matter

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  2. Theory

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      Pages 1-10

      Bayesian Approach to the Concept Drift in the Pattern Recognition Problems

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      Pages 11-25

      Transductive Relational Classification in the Co-training Paradigm

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      Pages 26-39

      Generalized Nonlinear Classification Model Based on Cross-Oriented Choquet Integral

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      Pages 40-49

      A General Lp-norm Support Vector Machine via Mixed 0-1 Programming

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      Pages 50-62

      Reduction of Distance Computations in Selection of Pivot Elements for Balanced GHT Structure

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      Pages 63-75

      Hot Deck Methods for Imputing Missing Data

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      Pages 76-85

      BINER

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      Pages 86-101

      A New Approach for Association Rule Mining and Bi-clustering Using Formal Concept Analysis

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      Pages 102-116

      Top-N Minimization Approach for Indicative Correlation Change Mining

  3. Theory: Evaluation of Models and Performance Evaluation Methods

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      Pages 117-131

      Selecting Classification Algorithms with Active Testing

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      Pages 132-140

      Comparing Logistic Regression, Neural Networks, C5.0 and M5′ Classification Techniques

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      Pages 141-153

      Unsupervised Grammar Inference Using the Minimum Description Length Principle

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      Pages 154-168

      How Many Trees in a Random Forest?

  4. Theory: Learning

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      Pages 169-182

      Constructing Target Concept in Multiple Instance Learning Using Maximum Partial Entropy

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      Pages 183-197

      A New Learning Structure Heuristic of Bayesian Networks from Data

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      Pages 198-212

      Discriminant Subspace Learning Based on Support Vectors Machines

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      Pages 213-221

      A New Learning Strategy of General BAMs

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      Pages 222-236

      Proximity-Graph Instance-Based Learning, Support Vector Machines, and High Dimensionality: An Empirical Comparison

  5. Theory: Clustering

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      Pages 237-251

      Semi Supervised Clustering: A Pareto Approach

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      Pages 252-263

      Semi-supervised Clustering: A Case Study

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