Neural Networks for Pattern Recognition
This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks,...
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This is the first comprehensive treatment of feed-forward neural networks from the perspective of statistical pattern recognition. After introducing the basic concepts, the book examines techniques for modeling probability density functions and the properties and merits of the multi-layer perceptron and radial basis function network models. Also covered are various forms of error functions, principal algorithms for error function minimalization, learning and generalization in neural networks, and Bayesian techniques and their applications. Designed as a text, with over 100 exercises, this fully up-to-date work will benefit anyone involved in the fields of neural computation and pattern recognition.
1Statistical Pattern Recognition12Probability Density Estimation333Single-Layer Networks774The Multi-layer Perceptron1165Radial Basis Functions1646Error Functions1947Parameter Optimization Algorithms2538Pre-processing and Feature Extraction2959Learning and Generalization33210Bayesian Techniques385Symmetric Matrices440Gaussian Integrals444Lagrange Multipliers448Calculus of Variations451Principal Components454References457Index477
