Partially Supervised Learning

1. Front Matter

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2. Invited Talks

   1. Unlabeled Data and Multiple Views

      • Zhi-Hua Zhou

      Pages 1-7

   2. Online Semi-supervised Ensemble Updates for fMRI Data

      • Catrin O. Plumpton

      Pages 8-18

   3. Studying Self- and Active-Training Methods for Multi-feature Set Emotion Recognition

      • José Esparza, Stefan Scherer, Friedhelm Schwenker

      Pages 19-31

3. Algorithms

   1. Semi-supervised Linear Discriminant Analysis Using Moment Constraints

      • Marco Loog

      Pages 32-41

   2. Manifold-Regularized Minimax Probability Machine

      • Kazuki Yoshiyama, Akito Sakurai

      Pages 42-51

   3. Supervised and Unsupervised Co-training of Adaptive Activation Functions in Neural Nets

      • Ilaria Castelli, Edmondo Trentin

      Pages 52-61

   4. Semi-unsupervised Weighted Maximum-Likelihood Estimation of Joint Densities for the Co-training of Adaptive Activation Functions

      • Ilaria Castelli, Edmondo Trentin

      Pages 62-71

   5. Semi-Supervised Kernel Clustering with Sample-to-Cluster Weights

      • Stefan Faußer, Friedhelm Schwenker

      Pages 72-81

   6. Homeokinetic Reinforcement Learning

      • Simón C. Smith, J. Michael Herrmann

      Pages 82-91

   7. Iterative Refinement of HMM and HCRF for Sequence Classification

      • Yann Soullard, Thierry Artieres

      Pages 92-95

4. Applications

   1. On the Utility of Partially Labeled Data for Classification of Microarray Data

      • Ludwig Lausser, Florian Schmid, Hans A. Kestler

      Pages 96-109

   2. Multi-instance Methods for Partially Supervised Image Segmentation

      • Andreas Müller, Sven Behnke

      Pages 110-119

   3. Semi-supervised Training Set Adaption to Unknown Countries for Traffic Sign Classifiers

      • Matthias Hillebrand, Christian Wöhler, Ulrich Kreßel, Franz Kummert

      Pages 120-127

   4. Comparison of Combined Probabilistic Connectionist Models in a Forensic Application

      • Edmondo Trentin, Luca Lusnig, Fabio Cavalli

      Pages 128-137

   5. Classification of Emotional States in a Woz Scenario Exploiting Labeled and Unlabeled Bio-physiological Data

      • Martin Schels, Markus Kächele, David Hrabal, Steffen Walter, Harald C. Traue, Friedhelm Schwenker

      Pages 138-147

   6. Using Self Organizing Maps to Find Good Comparison Universities

      • Cameron Cooper, Robert Kilmer

      Pages 148-153

   7. Sink Web Pages in Web Application

      • Doru Anastasiu Popescu

      Pages 154-158

5. Back Matter

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This book constitutes thoroughly refereed revised selected papers from the First IAPR TC3 Workshop on Partially Supervised Learning, PSL 2011, held in Ulm, Germany, in September 2011. The 14 papers presented in this volume were carefully reviewed and selected for inclusion in the book, which also includes 3 invited talks. PSL 2011 dealt with methodological issues as well as real-world applications of PSL. The main methodological issues were: combination of supervised and unsupervised learning; diffusion learning; semi-supervised classification, regression, and clustering; learning with deep architectures; active learning; PSL with vague, fuzzy, or uncertain teaching signals; learning, or statistical pattern recognition; and PSL in cognitive systems. Applications of PSL included: image and signal processing; multi-modal information processing; sensor/information fusion; human computer interaction; data mining and Web mining; forensic anthropology; and bioinformatics.

• Web application
• higher education
• neural networks
• pattern recognition
• self organizing maps
• algorithm analysis and problem complexity

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