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Neural Network Learning: Theoretical Foundations by Martin Anthony, Peter L. Bartlett

Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett ebook
Format: pdf
Publisher:
ISBN: 052111862X, 9780521118620
Page: 404

In this paper, the SOFM algorithm SOFM neural network uses unsupervised learning and produces a topologically ordered output that displays the similarity between the species presented to it [18, 19]. Neural Network Learning: Theoretical Foundations: Martin Anthony. ALT 2011 - PDF Preprint Papers | Sciweavers . HomePage Selected Books, Book Chapters. Artificial Neural Networks Mathematical foundations of neural networks. Cite as: arXiv:1303.0818 [cs.NE]. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. Because of its theoretical advantages, it is expected to apply Self-Organizing Feature Map to functional diversity analysis. 20120003110024) and the National Natural Science Foundation of China (Grant no. Product DescriptionThis important work describes recent theoretical advances in the study of artificial neural networks. The network consists of two layers, .. There are so many different books on Neural Networks: Amazon's Neural Network. Amazon.com: Neural Networks: Books Neural Network Learning: Theoretical Foundations by Martin Anthony and Peter L. 10th International Conference on Inductive Logic Programming,. Noise," International Conference on Algorithmic Learning Theory.