A hybrid learning system for image deblurring

In this paper we propose a 3-stage hybrid learning system with unsupervised learning to cluster data in the first stage, supervised learning in the middle stage to determine network parameters and finally a decision-making stage using voting mechanism. We take this opportunity to study the role of various supervised learning systems that constitute the middle stage. Specifically, we focus on one-hidden layer neural network with sigmoidal activation function, radial basis function network with Gaussian activation function and projection pursuit learning network with Hermite polynomial as the activation function. These learning systems rank in increasing order of complexity. We train and test each system with identical data sets. Experimental results show that learning ability of a system is controlled by the shape of the activation function when other parameters remain fixed. We observe that clustering in the input space leads to better system performance. Experimental results provide compelling evidences in favor of use of the hybrid learning system and the committee machines with gating network.