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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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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]. Bartlett — Neural Network Learning: Theoretical Foundations; M. 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. 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. Some titles of books I've been reading in the past two weeks: M. The network consists of two layers, .. Artificial neural networks, a biologically inspired computing methodology, have the ability to learn by imitating the learning method used in the human brain. 10th International Conference on Inductive Logic Programming,. Cheap This important work describes recent theoretical advances in the study of artificial neural networks. Biggs — Computational Learning Theory; L. Neural Network Learning: Theoretical Foundations: Martin Anthony. Share this I'm a bit of a freak – enterprise software team lead during the day and neural network researcher during the evening. 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.