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Neural Network Learning: Theoretical Foundations Martin Anthony, Peter L. Bartlett
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HomePage Selected Books, Book Chapters. Noise," International Conference on Algorithmic Learning Theory. 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. This important work describes recent theoretical advances in the study of artificial 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). ALT 2011 - PDF Preprint Papers | Sciweavers . Although this blog includes links to other Internet sites, it takes no responsibility for the content or information contained on those other sites, nor does it exert any editorial or other control over those other sites. At the end of the day it was decided that to wrap up all the discussions and move forward into designing the “Internet of Education” conference in 2013 as the yearly flagship conference of Knowledge 4 All Foundation Ltd. Learning theory (supervised/ unsupervised/ reinforcement learning) Knowledge based networks. Artificial Neural Networks Mathematical foundations of neural networks.