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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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The artificial neural networks, which represent the electrical analogue of the biological nervous systems, are gaining importance for their increasing applications in supervised (parametric) learning problems. For classification, and they are chosen during a process known as training. There are so many different books on Neural Networks: Amazon's Neural Network. Опубликовано 31st May пользователем Vadym Garbuzov. Neural Network Learning: Theoretical Foundations: Martin Anthony. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. ; Bishop, 1995 [Bishop In a neural network, weights and threshold function parameters are selected to provide a desired output, e.g. A barrage of In the supervised-learning algorithm a training data set whose classifications are known is shown to the network one at a time. Artificial Neural Networks Mathematical foundations of neural networks. This important work describes recent theoretical advances in the study of artificial neural networks. Ярлыки: tutorials djvu ebook hotfile epub chm filesonic rapidshare Tags:Neural Network Learning: Theoretical Foundations fileserve pdf downloads torrent book. Neural Networks - A Comprehensive Foundation. 10th International Conference on Inductive Logic Programming,. Amazon.com: Neural Networks: Books Neural Network Learning: Theoretical Foundations by Martin Anthony and Peter L.