Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006, Proceedings (Lecture Notes in Computer Science, 4087, Band 4087)

Schwenker, Friedhelm und Simone Marinai:

ISBN 10: 3540379517 / ISBN 13: 9783540379515

Editore: Springer, 2008

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308 Seiten; 9783540379515.3 Gewicht in Gramm: 1.

Codice articolo 972111

Dati bibliografici

• Titolo: Artificial Neural Networks in Pattern Recognition: Second IAPR Workshop, ANNPR 2006, Ulm, Germany, August 31-September 2, 2006, Proceedings (Lecture Notes in Computer Science, 4087, Band 4087)
• Editore: Springer
• Data di pubblicazione: 2008
• Lingua: Inglese
• ISBN 10: 3540379517
• ISBN 13: 9783540379515
• Rilegatura: paperback
• Condizione: Gut
• Peso: 1 grammo
• Cataloghi dei venditori:
  • Varia

Descrizione articolo

Riassunto

This book constitutes the refereed proceedings of the Second IAPR Workshop on Artificial Neural Networks in Pattern Recognition, ANNPR 2006, held in Ulm, Germany in August/September 2006. The 26 revised papers presented were carefully reviewed and selected from 49 submissions. The papers are organized in topical sections on unsupervised learning, semi-supervised learning, supervised learning, support vector learning, multiple classifier systems, visual object recognition, and data mining in bioinformatics.

Contenuti

Unsupervised Learning.- Simple and Effective Connectionist Nonparametric Estimation of Probability Density Functions.- Comparison Between Two Spatio-Temporal Organization Maps for Speech Recognition.- Adaptive Feedback Inhibition Improves Pattern Discrimination Learning.- Semi-supervised Learning.- Supervised Batch Neural Gas.- Fuzzy Labeled Self-Organizing Map with Label-Adjusted Prototypes.- On the Effects of Constraints in Semi-supervised Hierarchical Clustering.- A Study of the Robustness of KNN Classifiers Trained Using Soft Labels.- Supervised Learning.- An Experimental Study on Training Radial Basis Functions by Gradient Descent.- A Local Tangent Space Alignment Based Transductive Classification Algorithm.- Incremental Manifold Learning Via Tangent Space Alignment.- A Convolutional Neural Network Tolerant of Synaptic Faults for Low-Power Analog Hardware.- Ammonium Estimation in a Biological Wastewater Plant Using Feedforward Neural Networks.- Support Vector Learning.- Support Vector Regression Using Mahalanobis Kernels.- Incremental Training of Support Vector Machines Using Truncated Hypercones.- Fast Training of Linear Programming Support Vector Machines Using Decomposition Techniques.- Multiple Classifier Systems.- Multiple Classifier Systems for Embedded String Patterns.- Multiple Neural Networks for Facial Feature Localization in Orientation-Free Face Images.- Hierarchical Neural Networks Utilising Dempster-Shafer Evidence Theory.- Combining MF Networks: A Comparison Among Statistical Methods and Stacked Generalization.- Visual Object Recognition.- Object Detection and Feature Base Learning with Sparse Convolutional Neural Networks.- Visual Classification of Images by Learning Geometric Appearances Through Boosting.- An Eye Detection System Based on Neural Autoassociators.- Orientation Histograms for Face Recognition.- Data Mining in Bioinformatics.- An Empirical Comparison of Feature Reduction Methods in the Context of Microarray Data Classification.- Unsupervised Feature Selection for Biomarker Identification in Chromatography and Gene Expression Data.- Learning and Feature Selection Using the Set Covering Machine with Data-Dependent Rays on Gene Expression Profiles.