Handbook of Neural Computation: Supplement 1
In recent years, neural computation has developed from a specialized research discipline into a broadly based and dynamic activity with applications in an astonishing variety of fields. Many scientists, engineers and other practitioners are now using neural networks to tackle problems that are either intractable or unrealistically time consuming to solve through traditional computational strategies. The inaugural volume in the Computational Intelligence Library provides speedy dissemination of new ideas to a broad spectrum of neural network users, designers and implementers. Devoted to network fundamentals, models, algorithms and applications, the work is intended to become the standard reference resource for the neural network community. As the field expands and develops, leading researchers will report on an analyze promising new approaches. In this way, the Handbook will become an evolving compendium on the state of the art of neural computation. Available in loose-leaf print form as well as in an electronic edition that combines both CD-ROM and on-line (World Wide Web) access to its contents, the Handbook of Neural Computation is available on a subscription basis, with regularly published supplements keeping readers abreast of late-breaking developments and new advances in this rapidly developing field.
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Part F Contents Updated
Part G Contents Updated
Adaptive Advances in Neural analysis application artificial neural networks ARTMAP backpropagation changes in implied CID3 classification cognitive Computer Science connections converge correlation corresponding D S Touretzky effect elastic network energy function error exogenous variables feedforward Fiesler forecasting Fuzzy Genetic Algorithms geon gradient descent graph Grossberg hidden layer hidden neurons hidden units implied volatility incremental value independent set independent set problem learning linear link scheduling problem maximum independent set modular Moneyness Morgan Kaufmann Nadal network architecture Neural Computation neural model neural network model neurons node nonlinear ontogenic ontogenic methods ontogenic neural networks optimal output function parameters pattern performance prediction Proc procedure Professor of Computer random graph representations Research Rmse sample semantic significant simulated solutions structure subcoloring subsets Table tiling algorithm topology Touretzky TPU1 trading training set United Kingdom United Kingdom e-mail University USA e-mail vector visual volatility changes Voronoi diagrams weights