Emergence of Learning: An Approach to Coping with NP-Complete Problems in Learning
نویسندگان
چکیده
Various theoretical results show that learning in conventional feedforward neural networks such as multilayer perceptrons is NP-complete. In this paper we show that learning in min-max modular (M3) neural networks is tractable. The key to coping with NP-complete problems in M3 networks is to decompose a large-scale problem into a number of manageable, independent subproblems and to make the learning of a large-scale problem emerge from the learning of a number of related small subproblems.
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