Pattern Recognition Using Neural Networks

Name: Pattern Recognition Using Neural Networks
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Bol This work covers linear pattern recognition and its non-linear extension via neural networks from an algorithmic approach. The text explores multiple layered preceptrons and describes network types such as functional link, radial basis function, learning vector quantanization and self-organizing. Pattern Regcognition with Neural Networks covers traditional linear pattern recognition and its nonlinear extension via neural networks from an algorithmic approach. The author has written a real-world practical "why-and-how" text that provides a refreshing contrast to competing texts' thoeretical appraoch and "pie-in-the-sky" claims. The text explores mulitple layered preceptrons and describes network types such as functional link, radial basis function, learning vector quantanization and self-organizing. The author also discusses recent clustering methods. This text is suitable for an advanced undergraduate course in pattern recognition or neural networks, and is also useful as a reference and a resource.

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This work covers linear pattern recognition and its non-linear extension via neural networks from an algorithmic approach. The text explores multiple layered preceptrons and describes network types such as functional link, radial basis function, learning vector quantanization and self-organizing. Pattern Regcognition with Neural Networks covers traditional linear pattern recognition and its nonlinear extension via neural networks from an algorithmic approach. The author has written a real-world practical "why-and-how" text that provides a refreshing contrast to competing texts' thoeretical appraoch and "pie-in-the-sky" claims. The text explores mulitple layered preceptrons and describes network types such as functional link, radial basis function, learning vector quantanization and self-organizing. The author also discusses recent clustering methods. This text is suitable for an advanced undergraduate course in pattern recognition or neural networks, and is also useful as a reference and a resource.

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