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arXiv · 2009.08932

Multi-Activation Hidden Units for Neural Networks with Random Weights

Ajay M. Patrikar
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Abstract

Single layer feedforward networks with random weights are successful in a variety of classification and regression problems. These networks are known for their non-iterative and fast training algorithms. A major drawback of these networks is that they require a large number of hidden units. In this paper, we propose the use of multi-activation hidden units. Such units increase the number of tunable parameters and enable formation of complex decision surfaces, without increasing the number of hidden units. We experimentally show that multi-activation hidden units can be used either to improve the classification accuracy, or to reduce computations.

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Ajay M. Patrikar. 2020-09-06. Multi-Activation Hidden Units for Neural Networks with Random Weights. https://arxiv.org/abs/2009.08932

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