Abstract
This paper presents a new algorithm to improve the learning algorithm of Mixture of Experts (ME) model by using Conjugate Gradient (CG) as a second order optimization technique. The CG technique is combined with Back-Propagation (BP) algorithm to yield an efficient learning algorithm for ME structure. The experts and gating network in the enhanced model is replaced with CG based Multi-Layer Perceptrons (MLPs), in order to provide faster and more accurate learning algorithm. The performance of proposed method is compared with Gradient Decent based ME (GD-ME) in several classification and regression problems. The results show that CG based ME (CG-ME) has faster convergence and better performance in the utilized benchmark data sets.
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