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

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

20120003110024) and the National Natural Science Foundation of China (Grant no. Subjects: Neural and Evolutionary Computing (cs.NE); Information Theory (cs.IT); Learning (cs.LG); Differential Geometry (math.DG). Cite as: arXiv:1303.0818 [cs.NE]. 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]. Cheap This important work describes recent theoretical advances in the study of artificial neural networks. The network consists of two layers, .. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. For beginners it is a nice introduction to the subject, for experts a valuable reference. My guess is that these patterns will not only be useful for machine learning, but also any other computational work that involves either a) processing large amounts of data, or b) algorithms that take a significant amount of time to execute. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. 'The book is a useful and readable mongraph. Artificial Neural Networks Mathematical foundations of neural networks. Because of its theoretical advantages, it is expected to apply Self-Organizing Feature Map to functional diversity analysis.