Isbn: 9780195079203 - pattern recognition using neural networks: theory and algorithms for engineers and scientists (10 resultados)

Condición: Gut. Zustand: Gut | Seiten: 480 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Condición: Sehr gut. Zustand: Sehr gut | Seiten: 480 | Sprache: Englisch | Produktart: Bücher | Keine Beschreibung verfügbar.

Hardcover. Condición: gut. 1997. Pattern Recognition Using Neural Networks: Theory and Algorithms for Engineers and Scientists In englischer Sprache. pages.

Condición: Very Good. Pages intact with possible writing/highlighting. Binding strong with minor wear. Dust jackets/supplements may not be included. Stock photo provided. Product includes identifying sticker. Better World Books: Buy Books. Do Good.

Condición: New. KlappentextrnrnPattern Recognition Using Neural Networks covers traditional linear pattern recognition and its nonlinear extension via neural networks. The approach is algorithmic for easy implementation on a computer, which makes this a refres.

Buch. Condición: Neu. Neuware - Pattern Recognition Using Neural Networks covers traditional linear pattern recognition and its nonlinear extension via neural networks. The approach is algorithmic for easy implementation on a computer, which makes this a refreshing what-why-and-how text that contrasts with the theoretical approach and pie-in-the-sky hyperbole of many books on neural networks. It covers the standard decision-theoretic pattern recognition of clustering via minimumdistance, graphical and structural methods, and Bayesian discrimination. Looney has written a graduate level textbook combining the fields of pattern recognition and neural networks. It contains some theory of why the most useful networks work, the pitfalls, the algorithms to implement them, and their applications. This text is suitable for an advanced undergraduate or graduate level course in pattern recognition or neural networks for students in computer science or electrical and computer engineering. It is also useful as a reference and a resource for practitioners and researchers.…

Condición: New. Print on Demand pp. 480.

Condición: New. Print on Demand pp. 480.

Condición: New. PRINT ON DEMAND pp. 480.

Hardcover. Condición: new. Hardcover. Pattern Recognition Using Neural Networks covers traditional linear pattern recognition and its nonlinear extension via neural networks. The approach is algorithmic for easy implementation on a computer, which makes this a refreshing what-why-and-how text that contrasts with the theoretical approach and pie-in-the-sky hyperbole of many books on neural networks. It covers the standard decision-theoretic pattern recognition of clustering via minimum distance,graphical and structural methods, and Bayesian discrimination.Pattern recognizers evolve across the sections into perceptrons, a layer of perceptrons, multiple-layered perceptrons, functionallink nets, and radial basis function networks. Other networks covered in the process are learning vector quantization networks, self-organizing maps, and recursive neural networks. Backpropagation is derived in complete detail for one and two hidden layers for both unipolar and bipolar sigmoid activation functions. The more efficient fullpropagation, quickpropagation, cascade correlation, and various methods such as strategic search, conjugate gradients, and genetic algorithms are described.Advanced methods are also described, including the full training algorithms for radial basis function networks and random vector functional link nets, as well as competitive learning networks and fuzzyclustering algorithms.Special topics covered include:feature engineeringdata engineeringneural engineering of network architecturesvalidation and verification of the trained networksThis textbook is ideally suited for a senior undergraduate or graduate course in pattern recognition or neural networks for students in computer science, electrical engineering, and computer engineering. It is also a useful reference andresource for researchers and professionals. A text covering traditional linear pattern recognition and its nonlinear extension via neural networks. The approach is algorithmic for easy implementation on a computer. It covers the standard decision-theoretic pattern recognition of clustering via minimum distance, graphical and structural methods, and Bayesian discrimination. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…