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Neural Networks: a classroom approach
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| Format: | Printed Book |
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Chennai
MGH
2013
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| Edition: | 2nd ed. |
Table of Contents:
- 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