Artificial Neural Networks and Machine Learning -- ICANN 2012

1. Inference and Recognition (C5)

   1. Nested Sequential Minimal Optimization for Support Vector Machines

      • Alessandro Ghio, Davide Anguita, Luca Oneto, Sandro Ridella, Carlotta Schatten

      Pages 156-163

   2. Random Subspace Method and Genetic Algorithm Applied to a LS-SVM Ensemble

      • Carlos Padilha, Adrião Dória Neto, Jorge Melo

      Pages 164-171

   3. Text Recognition in Videos Using a Recurrent Connectionist Approach

      • Khaoula Elagouni, Christophe Garcia, Franck Mamalet, Pascale Sébillot

      Pages 172-179

   4. An Investigation of Ensemble Systems Applied to Encrypted and Cancellable Biometric Data

      • Isaac de L. Oliveira Filho, Benjamn R. C. Bedregal, Anne M. P. Canuto

      Pages 180-188

   5. New Dynamic Classifiers Selection Approach for Handwritten Recognition

      • Nabiha Azizi, Nadir Farah, Abdel Ennaji

      Pages 189-196

   6. Vector Perceptron Learning Algorithm Using Linear Programming

      • Vladimir Kryzhanovskiy, Irina Zhelavskaya, Anatoliy Fonarev

      Pages 197-204

   7. A Robust Objective Function of Joint Approximate Diagonalization

      • Yoshitatsu Matsuda, Kazunori Yamaguchi

      Pages 205-212

   8. TrueSkill-Based Pairwise Coupling for Multi-class Classification

      • Jong-Seok Lee

      Pages 213-220

   9. Analogical Inferences in the Family Trees Task: A Review

      • Sergio Varona-Moya, Pedro L. Cobos

      Pages 221-228

   10. An Efficient Way of Combining SVMs for Handwritten Digit Recognition

       • Renata F. P. Neves, Cleber Zanchettin, Alberto N. G. Lopes Filho

       Pages 229-237

   11. Comparative Evaluation of Regression Methods for 3D-2D Image Registration

       • Ana Isabel Rodrigues Gouveia, Coert Metz, Luís Freire, Stefan Klein

       Pages 238-245

   12. A MDRNN-SVM Hybrid Model for Cursive Offline Handwriting Recognition

       • Byron Leite Dantas Bezerra, Cleber Zanchettin, Vinícius Braga de Andrade

       Pages 246-254

   13. Extraction of Prototype-Based Threshold Rules Using Neural Training Procedure

       • Marcin Blachnik, Mirosław Kordos, Włodzisław Duch

       Pages 255-262

   14. Instance Selection with Neural Networks for Regression Problems

       • Mirosław Kordos, Marcin Blachnik

       Pages 263-270

   15. A New Distance for Probability Measures Based on the Estimation of Level Sets

       • Alberto Muñoz, Gabriel Martos, Javier Arriero, Javier Gonzalez

       Pages 271-278

   16. Low Complexity Proto-Value Function Learning from Sensory Observations with Incremental Slow Feature Analysis

       • Matthew Luciw, Juergen Schmidhuber

       Pages 279-287

   17. Improving Neural Networks Classification through Chaining

       • Khobaib Zaamout, John Z. Zhang

       Pages 288-295

   18. Feature Ranking Methods Used for Selection of Prototypes

       • Marcin Blachnik, Włodzisław Duch, Tomasz Maszczyk

       Pages 296-304

   19. A “Learning from Models” Cognitive Fault Diagnosis System

       • Cesare Alippi, Manuel Roveri, Francesco Trovò

       Pages 305-313

2. Support Vector Machines (A5)

   1. Improving ANNs Performance on Unbalanced Data with an AUC-Based Learning Algorithm

      • Cristiano L. Castro, Antônio P. Braga

      Pages 314-321

The two-volume set LNCS 7552 + 7553 constitutes the proceedings of the 22nd International Conference on Artificial Neural Networks, ICANN 2012, held in Lausanne, Switzerland, in September 2012. The 162 papers included in the proceedings were carefully reviewed and selected from 247 submissions. They are organized in topical sections named: theoretical neural computation; information and optimization; from neurons to neuromorphism; spiking dynamics; from single neurons to networks; complex firing patterns; movement and motion; from sensation to perception; object and face recognition; reinforcement learning; bayesian and echo state networks; recurrent neural networks and reservoir computing; coding architectures; interacting with the brain; swarm intelligence and decision-making; mulitlayer perceptrons and kernel networks; training and learning; inference and recognition; support vector machines; self-organizing maps and clustering; clustering, mining and exploratory analysis; bioinformatics; and time weries and forecasting.

• brain-computer interface
• combinatorial optimization
• evolutionary algorithm
• particle swarm
• self-organizing maps
• algorithm analysis and problem complexity

• Neuro Heuristic Research Group, University of Lausanne, Lausanne, Switzerland

  Alessandro E. P. Villa

• Department of Informatics, Nicolaus Copernicus University, Toruń, Poland

  Włodzisław Duch

• Center for Complex Systems Studies, Kalamazoo College, Kalamazoo, USA

  Péter Érdi

• Dipartimento di Informatica e Scienze dell’Informazione, Università di Genova, Genoa, Italy

  Francesco Masulli

• Institut für Neuroinformatik, Universität Ulm, Ulm, Germany

  Günther Palm

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