Artificial neural networks in pattern recognition

InhaltsverzeichnisUnsupervised 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.