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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., Comment: 4 pages, 4 figures. arXiv admin note: substantial text overlap with arXiv:2008.10425

Single layer feedforward networks with random weights are known for their non-iterative and fast training algorithms and are successful in a variety of classification and regression problems. A major drawback of these networks is that they require a large number of hidden units. In this paper, we propose a technique to reduce the number of hidden units substantially without affecting the accuracy of the networks significantly. We introduce the concept of primary and secondary hidden units. The weights for the primary hidden units are chosen randomly while the secondary hidden units are derived using pairwise combinations of the primary hidden units. Using this technique, we show that the number of hidden units can be reduced by at least one order of magnitude. We experimentally show that this technique leads to significant drop in computations at inference time and has only a minor impact on network accuracy. A huge reduction in computations is possible if slightly lower accuracy is acceptable., Comment: 5 pages, 8 figures