Titlebook: Mathematics of Neural Networks; Models, Algorithms a Stephen W. Ellacott,John C. Mason,Iain J. Anderson Book 1997 Springer Science+Business

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Monica Bianchini,Stefano Fanelli,Marco Gori,Marco Protasien at the University of Hertfordshire and the University of Bergamo. It deals with the theory behind numerical methods for nonlinear optimization and their application to a range of problems in science and engineering. The book is intended for ?nal year undergraduate students in mathematics (or othe

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Jan C. Bioch,Robert Carsouw,Rob Potharsten at the University of Hertfordshire and the University of Bergamo. It deals with the theory behind numerical methods for nonlinear optimization and their application to a range of problems in science and engineering. The book is intended for ?nal year undergraduate students in mathematics (or othe

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Christopher M. Bishop,Ian T. Nabneyen at the University of Hertfordshire and the University of Bergamo. It deals with the theory behind numerical methods for nonlinear optimization and their application to a range of problems in science and engineering. The book is intended for ?nal year undergraduate students in mathematics (or othe

勾引

A Non-Equidistant Elastic Net AlgorithmThe statistical mechanical derivation by Simic of the Elastic Net Algorithm (ENA) from a stochastic Hopfield neural network is criticized. In our view, the ENA should be considered a .. Using a linear distance measure, a . Elastic Net Algorithm (NENA) is presented. Finally, a . Elastic Net Algorithm (HENA) is discussed.

BAIL

On the Use of Simple Classifiers for the Initialisation of One-Hidden-Layer Neural NetsLinear decision tree classifiers and LVQ-networks divide the input space into convex regions that can be represented by membership functions. These functions are then used to determine the weights of the first layer of a feedforward network. Subject classification: AMS(MOS) 68T05, 92B20

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Operations Research/Computer Science Interfaces Serieshttp://image.papertrans.cn/m/image/626951.jpg

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Modelling Conditional Probability Distributions for Periodic Variables paper we introduce three related techniques for tackling such problems, and test them using synthetic data. We then apply them to the problem of extracting the distribution of wind vector directions from radar scatterometer data.