Machine Learning and Data Mining in Pattern Recognition

1. Front Matter

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2. Classification and Decision Theory

   1. Quadratically Constrained Maximum a Posteriori Estimation for Binary Classifier

      • Tatsuya Yokota, Yukihiko Yamashita

      Pages 1-15

   2. Hubness-Based Fuzzy Measures for High-Dimensional k-Nearest Neighbor Classification

      • Nenad Tomašev, Miloš Radovanović, Dunja Mladenić, Mirjana Ivanović

      Pages 16-30

   3. Decisions: Algebra and Implementation

      • Antonina Danylenko, Jonas Lundberg, Welf Löwe

      Pages 31-45

   4. Smoothing Multinomial Naïve Bayes in the Presence of Imbalance

      • Alexander Y. Liu, Cheryl E. Martin

      Pages 46-59

   5. ACE-Cost: Acquisition Cost Efficient Classifier by Hybrid Decision Tree with Local SVM Leaves

      • Liyun Li, Umut Topkara, Nasir Memon

      Pages 60-74

   6. Informative Variables Selection for Multi-relational Supervised Learning

      • Dhafer Lahbib, Marc Boullé, Dominique Laurent

      Pages 75-87

   7. Separability of Split Value Criterion with Weighted Separation Gains

      • Krzysztof Grąbczewski

      Pages 88-98

   8. Granular Instances Selection for Fuzzy Modeling

      • S. Sakinah S. Ahmad, Witold Pedrycz

      Pages 99-111

   9. Parameter-Free Anomaly Detection for Categorical Data

      • Shu Wu, Shengrui Wang

      Pages 112-126

   10. Fuzzy Semi-supervised Support Vector Machines

       • Houda Benbrahim

       Pages 127-139

   11. GENCCS: A Correlated Group Difference Approach to Contrast Set Mining

       • Mondelle Simeon, Robert Hilderman

       Pages 140-154

   12. Collective Classification Using Heterogeneous Classifiers

       • Zehra Cataltepe, Abdullah Sonmez, Kadriye Baglioglu, Ayse Erzan

       Pages 155-169

   13. Spherical Nearest Neighbor Classification: Application to Hyperspectral Data

       • Dalton Lunga, Okan Ersoy

       Pages 170-184

   14. Adaptive Kernel Diverse Density Estimate for Multiple Instance Learning

       • Tao Xu, Iker Gondra, David Chiu

       Pages 185-198

   15. Boosting Inspired Process for Improving AUC

       • Victor S. Sheng, Rahul Tada

       Pages 199-209

3. Theory of Learning

   1. Investigation in Transfer Learning: Better Way to Apply Transfer Learning between Agents

      • Luiz Antonio Celiberto Junior, Jackson P. Matsuura

      Pages 210-223

   2. Exploration Strategies for Learned Probabilities in Smart Terrain

      • John Sullins

      Pages 224-238

   3. Sensitivity Analysis for Weak Constraint Generation

      • Jamshaid G. Mohebzada, Michael M. Richter, Guenther Ruhe

      Pages 239-252

   4. Dictionary Learning Based on Laplacian Score in Sparse Coding

      • Jin Xu, Hong Man

      Pages 253-264

This book constitutes the refereed proceedings of the 7th International Conference on Machine Learning and Data Mining in Pattern Recognition, MLDM 2011, held in New York, NY, USA. The 44 revised full papers presented were carefully reviewed and selected from 170 submissions. The papers are organized in topical sections on classification and decision theory, theory of learning, clustering, application in medicine, webmining and information mining; and machine learning and image mining.

• algorithmic learning
• data analysis
• kernel methods
• suppor vector machine
• text analysis

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

  Petra Perner

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