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Stochastic connection neural networks

Zhao, J; Shawe-Taylor, J; (1995) Stochastic connection neural networks. In: UNSPECIFIED (pp. 35-39).

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Abstract

In this paper we investigate a novel neural network model which uses stochastic weights. It is shown that the functionality of the network is comparable to that of a general stochastic neural network using standard sigmoid activation functions. For the multi-layer feedforward structure we demonstrate the network can be successfully used to solve a real problem like handwritten digit recognition. It is also shown that the recurrent network is as powerful as a Boltzmann machine. A new technique to implement simulated annealing is presented. Simulation results on the graph bisection problem demonstrate the model is efficient for global optimization.

Type: Book chapter
Title: Stochastic connection neural networks
UCL classification: UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: http://discovery.ucl.ac.uk/id/eprint/79231
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