A fast and convergent stochastic MLP learning algorithm.

A Sakurai

doi:10.1142/s0129065701000977

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A fast and convergent stochastic MLP learning algorithm.

A Sakurai

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Abstract

We propose a stochastic learning algorithm for multilayer perceptrons of linear-threshold function units, which theoretically converges with probability one and experimentally exhibits 100% convergence rate and remarkable speed on parity and classification problems with typical generalization accuracy. For learning the n bit parity function with n hidden units, the algorithm converged on all the trials we tested (n=2 to 12) after 5.8 x 4.1(n) presentations for 0.23 x 4.0(n-6) seconds on a 533MHz Alpha 21164A chip on average, which is five to ten times faster than Levenberg-Marquardt algorithm with restarts. For a medium size classification problem known as Thyroid in UCI repository, the algorithm is faster in speed and comparative in generalization accuracy than the standard backpropagation and Levenberg-Marquardt algorithms.

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Algorithms Artificial Intelligence Computer Simulation Learning Linear Models Models, Neurological Neural Networks, Computer Signal Processing, Computer-Assisted Stochastic Processes Time Comparative Study Journal Article

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A Sakurai. 2001. A fast and convergent stochastic MLP learning algorithm.. https://doi.org/10.1142/s0129065701000977

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