Neural Network Learning 2024 pdf epub mobi 電子書 下載


Neural Network Learning

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This important work describes recent theoretical advances in the study of artificial neural networks. It explores probabilistic models of supervised learning problems, and addresses the key statistical and computational questions. Chapters survey research on pattern classification with binary-output networks, including a discussion of the relevance of the Vapnik Chervonenkis dimension, and of estimates of the dimension for several neural network models. In addition, Anthony and Bartlett develop a model of classification by real-output networks, and demonstrate the usefulness of classification with a "large margin." The authors explain the role of scale-sensitive versions of the Vapnik Chervonenkis dimension in large margin classification, and in real prediction. Key chapters also discuss the computational complexity of neural network learning, describing a variety of hardness results, and outlining two efficient, constructive learning algorithms. The book is self-contained and accessible to researchers and graduate students in computer science, engineering, and mathematics.

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Neural Network Learning 2024 pdf epub mobi 電子書 下載

Neural Network Learning 2024 pdf epub mobi 電子書 下載

Neural Network Learning 2024 pdf epub mobi 電子書 下載



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出版者:Cambridge University Press
作者:Martin Anthony
出品人:
頁數:404
譯者:
出版時間:2009-8-20
價格:USD 59.00
裝幀:Paperback
isbn號碼:9780521118620
叢書系列:

圖書標籤: 機器學習  神經網絡  ML  計算機科學  Neural_Networks  NeuralNetwork   


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