Abstract
We introduced a Square-law based RBF kernel called SQuare RBF (SQ-RBF) which is computationally efficient and effective due to the elimination of the exponential term. In contrast to the Gaussian RBF, SQ-RBF requires smaller computational operation count and direct implementation without a call to higher order library. The derivative of the SQ-RBF is linear which will improve gradient computation and makes its applicability in multilayer perceptron neural network attractive. In experiments, SQ-RBF lead not only to faster learning but also requires significant low neurons than Gaussian RBF on networks. On an average, we recorded a speed-up in training time of about 8% for SQ-RBF based networks without affecting the overall generalizability of the network. SQ-RBF uses about 10% fewer neurons than Gaussian RBF hence making it very attractive.
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Wuraola, A., Patel, N. (2018). Computationally Efficient Radial Basis Function. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11302. Springer, Cham. https://doi.org/10.1007/978-3-030-04179-3_9
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