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arXiv · 1702.05639

Deep Stochastic Configuration Networks with Universal Approximation Property

Dianhui Wang·Ming Li
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Abstract

This paper develops a randomized approach for incrementally building deep neural networks, where a supervisory mechanism is proposed to constrain the random assignment of the weights and biases, and all the hidden layers have direct links to the output layer. A fundamental result on the universal approximation property is established for such a class of randomized leaner models, namely deep stochastic configuration networks (DeepSCNs). A learning algorithm is presented to implement DeepSCNs with either specific architecture or self-organization. The read-out weights attached with all direct links from each hidden layer to the output layer are evaluated by the least squares method. Given a set of training examples, DeepSCNs can speedily produce a learning representation, that is, a collection of random basis functions with the cascaded inputs together with the read-out weights. An empirical study on a function approximation is carried out to demonstrate some properties of the proposed deep learner model.

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Dianhui Wang, Ming Li. 2018-03-16. Deep Stochastic Configuration Networks with Universal Approximation Property. https://arxiv.org/abs/1702.05639

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