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Pattern Recognition and Neural Networks


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This book uses tools from statistical decision theory and computational learning theory to create a rigorous foundation for the theory of neural networks. On the theoretical side, Pattern Recognition and Neural Networks emphasizes probability and statistics. In addition to the more common type of network, this book has chapters on decision trees and belief networks from the field of machine-learning. This book is intended for use in graduate courses that teach statistics and engineering.

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data mining machine learning networks neural networks pattern recognition statistics science computational neuroscience applied mathematics artificial neural network bayesian network

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