模式識別中的人工神經(jīng)網(wǎng)絡(luò)

內(nèi)容概要

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.

書籍目錄

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 LearningSemi-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 LabelsSupervised 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 NetworksSupport 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 TechniquesMultiple 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 GeneralizationVisual Object Recognition  Object Detection and Feature Base Learning with Sparse Convolutional Neural Networks  ……Data Mining in BioinformaticsAuthor Index

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