模式识别中的人工神经网络
2006-12
湖北辞书出版社
Schwenker, Friedhelm; Marinai, Simone;
297
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.
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
