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Machine Learning and Data Mining in Pattern Recognition

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

  • Conference proceedings
  • © 2012

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Editors:
  1. Petra Perner

    1. Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany

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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 7376)

Part of the book sub series: Lecture Notes in Artificial Intelligence (LNAI)

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Conference proceedings info: MLDM 2012.

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About this book

This book constitutes the refereed proceedings of the 8th International Conference, MLDM 2012, held in Berlin, Germany in July 2012. The 51 revised full papers presented were carefully reviewed and selected from 212 submissions. The topics range from theoretical topics for classification, clustering, association rule and pattern mining to specific data mining methods for the different multimedia data types such as image mining, text mining, video mining and web mining.

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Table of contents (51 papers)

  1. Front Matter

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  2. Theory

    1. Bayesian Approach to the Concept Drift in the Pattern Recognition Problems

      • Pavel Turkov, Olga Krasotkina, Vadim Mottl
      Pages 1-10

    2. Transductive Relational Classification in the Co-training Paradigm

      • Michelangelo Ceci, Annalisa Appice, Herna L. Viktor, Donato Malerba, Eric Paquet, Hongyu Guo
      Pages 11-25

    3. Generalized Nonlinear Classification Model Based on Cross-Oriented Choquet Integral

      • Rong Yang, Zhenyuan Wang
      Pages 26-39

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

      • Hai Thanh Nguyen, Katrin Franke
      Pages 40-49

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

      • László Kovács
      Pages 50-62

    6. Hot Deck Methods for Imputing Missing Data

      • Dieter William Joenssen, Udo Bankhofer
      Pages 63-75

    7. BINER

      • Saket Bharambe, Harshit Dubey, Vikram Pudi
      Pages 76-85

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

      • Kartick Chandra Mondal, Nicolas Pasquier, Anirban Mukhopadhyay, Ujjwal Maulik, Sanghamitra Bandhopadyay
      Pages 86-101

    9. Top-N Minimization Approach for Indicative Correlation Change Mining

      • Aixiang Li, Makoto Haraguchi, Yoshiaki Okubo
      Pages 102-116

  3. Theory: Evaluation of Models and Performance Evaluation Methods

    1. Selecting Classification Algorithms with Active Testing

      • Rui Leite, Pavel Brazdil, Joaquin Vanschoren
      Pages 117-131

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

      • Amit Thombre
      Pages 132-140

    3. Unsupervised Grammar Inference Using the Minimum Description Length Principle

      • Upendra Sapkota, Barrett R. Bryant, Alan Sprague
      Pages 141-153

    4. How Many Trees in a Random Forest?

      • Thais Mayumi Oshiro, Pedro Santoro Perez, José Augusto Baranauskas
      Pages 154-168

  4. Theory: Learning

    1. Constructing Target Concept in Multiple Instance Learning Using Maximum Partial Entropy

      • Tao Xu, David Chiu, Iker Gondra
      Pages 169-182

    2. A New Learning Structure Heuristic of Bayesian Networks from Data

      • Heni Bouhamed, Afif Masmoudi, Thierry Lecroq, Ahmed Rebaï
      Pages 183-197

    3. Discriminant Subspace Learning Based on Support Vectors Machines

      • Nikolaos Pitelis, Anastasios Tefas
      Pages 198-212

    4. A New Learning Strategy of General BAMs

      • Nong Thi Hoa, Bui The Duy
      Pages 213-221

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

      • Godfried T. Toussaint, Constantin Berzan
      Pages 222-236

  5. Theory: Clustering

    1. Semi Supervised Clustering: A Pareto Approach

      • Javid Ebrahimi, Mohammad Saniee Abadeh
      Pages 237-251
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Other volumes

  1. Machine Learning and Data Mining in Pattern Recognition

Editors and Affiliations

  • Institute of Computer Vision and Applied Computer Sciences, IBaI, Leipzig, Germany

    Petra Perner

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