Handbook of Neural Computation
| Type of publication: | Book |
| Citation: | Fiesler-96.1 |
| Series: | The Computational Intelligence Library |
| Year: | 1996 |
| Publisher: | Institute of Physics and Oxford University Press |
| Address: | New York, New York |
| Note: | The electronic version is expected in early 1997. |
| ISBN: | 0-7503-0312--3 and 0-7503-0413-8 |
| Abstract: | Many scientists and engineers now use neural networks to tackle problems that are either intractable, or unrealistically time consuming to solve, through traditional computational strategies. To address the need for speedy dissemination of new ideas in this field to a broad spectrum of neural network users, designers and implementers, Oxford University Press and the Institute of Physics have joined forces to create a major reference publication devoted to neural network fundamentals, models, algorithms, applications and implementations. This work is intended to become the standard reference resource for the neural network community. The Handbook of Neural Computation will be produced in parallel in two updatable formats, looseleaf paper and CD-ROM, and will be kept up to date by means of supplements published on a regular basis. Details of new architectures, algorithms and applications may be submitted to the Handbook editors for peer review and possible inclusion in a future supplement to the Handbook. In this way we will create a moving compendium of the state of the art of neural computation. Key features of the Handbook of Neural Computation: * A hands-on guide to the design and implementation of neural networks * A comprehensive source of reference for all neural network users, designers and implementers * Provides an information pathway between scientists and engineers in different disciplines who apply neural networks to generically similar problems * Provides timely information in a rapidly changing field |
| Userfields: | ipdmembership={learning}, language={English}, |
| Keywords: | adalaine, adaptive resonance theory, application, artificial neural network, associative memory, backpropagation, bidirectional associative memory, case study, combinatorial optimization, connectionism, connectionist network, control, data compression, feedback network, feedforward network, functional-link network, fundamentals, fuzzy-neural system, hardware implementation, Hopfield network, hybrid system, image processing, LVQ, madalaine, modelling of cognitive phenomena, multilayer perceptron, neocognitron, network analysis, neural computation, neural computing, neural expert system, neural network, neural-evolutionary system, ontogenic neural network, pattern classification, perceptron, prediction, radial basis function, recurrent neural network, self-organizing feature map, signal processing, software implementation, speech processing, supervised learning, time series analysis, topology, training, unsupervised learning |
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Idiap |
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| Added by: | [UNK] |
| Total mark: | 0 |
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