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
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.