Neural networks and learning machines/ Simon Haykin
Material type:
TextPublication details: New York: Prentice Hall, 2009Edition: 3rdDescription: 540 pages; 18 cmISBN: - 9780131471399
- 0131471392
- 23 006.32019 H419
| Item type | Current library | Call number | Status | Notes | Date due | Barcode | Item holds | |
|---|---|---|---|---|---|---|---|---|
| Books | ISI Library, Kolkata | 006.32019 H419 (Browse shelf(Opens below)) | Available | Gifted by Ashish Kumar(Student of M.Tech.(CS)) | C27408 |
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| 006.32 Z96 Introduction to artificial neural systems | 006.32 Z96 Introduction to artificial neural systems | 006.32019 H419 Neural networks and learning machines/ | 006.32019 H419 Neural networks and learning machines/ | 006.33 At887(93) Logdata + : deductive databases with complex objects | 006.33 B574 Fuzzy sets in approximate reasoning and information systems | 006.33 B574 Fuzzy sets in approximate reasoning and information system |
This book is completed in two parts. The 1st part contains up to chapter 7.
Includes bibliography and index
Principal-components analysis -- Self-organizing maps -- Information-theoretic learning models -- Stochastic methods rooted in statistical mechanics -- Dynamic programming -- Neurodynamics -- Bayseian filtering for state estimation of dynamic systems -- Dynamically driven recurrent networks
For graduate-level neural network courses offered in the departments of Computer Engineering, Electrical Engineering, and Computer Science. Renowned for its thoroughness and readability, this well-organized and completely up-to-date text remains the most comprehensive treatment of neural networks from an engineering perspective. Refocused, revised and renamed to reflect the duality of neural networks and learning machines, this edition recognizes that the subject matter is richer when these topics are studied together. Ideas drawn from neural networks and machine learning are hybridized to perform improved learning tasks beyond the capability of either independently.
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