  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.
