Neural Networks- A Classroom Approach

Part I: Traces of History and a Neuroscience Briefer 1
Chapter 1: The Brain Metaphor
Chapter 2: Lessons from Neuroscience
Part II: Feedforward Neural Networks and Supervised Learning
Chapter 3: Artificial Neurons, Neural Networks and Architectures
Chapter 4: Geometry of Binary Threshold Neurons and Their Networks
Chapter 5: Supervised Learning I: Perceptrons and LMS
Chapter 6: Supervised Learning II: Backpropagation and Beyond
Chapter 7: Neural Networks: A Statistical Pattern Recognition Perspective
Chapter 8: Statistical Learning Theory, Support Vector Machines and Radial Basis Function Networks
Part III: Recurrent Neurodynamical Systems and Unsupervised Learning
Chapter 9: Dynamical Systems Review
Chapter 10: Attractor Neural Networks
Chapter 11: Adaptive Resonance Theory
Chapter 12: Towards the Self-organizing Feature Map
Part IV: Contemporary Topics
Chapter 13: Fuzzy Sets and Fuzzy Systems
Chapter 14: Evolutionary Algorithms
Chapter 15: Soft Computing Goes Hybrid
Chapter 16: Frontiers of Research: Spiking and Quantum Neural Networks

This revised edition of Neural Networks is an up-to-date exposition of the subject andcontinues to provide an understanding of the underlying geometry of foundation neuralnetwork models while stressing on heuristic explanations of theoretical results. The

• Neural Networks