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

Multi-Activation Hidden Units for Neural Networks with Random Weights