Guaranteed Convergence of Learning in Neural Networks
This paper describes schedules for the learning parameter that guarantee convergence to the optimal solution. It focuses on the diifference between local and global optimization, i.e., learning in the presence of just one minimum and learning in the presence of several minima. In case of one minimum, the fastest possible cooling is an algebraic function of the number of learning steps, whereas in case of several minima the cooling must be “exponentially slow”.
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