Abstract
Part I FUNDAMENTALS OF PATTERN RECOGNITION 0. Basic Concepts of Pattern Recognition 1. Decision Theoretic Algorithms 2. Structural Pattern Recognition Part II INTRODUCTORY NEURAL NETWORKS 3. Artificial Neural Network Structures 4. Supervised Training via Error Backpropogation: Derivations 5. Acceleration and Stabilization of Supervised Gradient Training of MLPs Part III ADVANCED FUNDAMENTALS OF NEURAL NETWORKS 6. Supervised Training via Strategic Search 7. Advances in Network Algorithms for Recognition 8. Using Hopfield Recurrent Neural Networks Part IV NEURAL, FEATURE, AND DATA ENGINEERING 9. Neural Engineering and Testing of FANNs 10. Feature and Data Engineering
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