Automatic Kernel Construction During the Neural Network Learning by Modified Fast Singular Value Decomposition

Thanks to the broad application fields, learning neural networks is still a more significant problem nowadays. Any attempt in the construction of faster learning algorithms is highly well come. This article presents a new way of learning neural networks with kernels with modified pseudo-inverse learning by modified SVD. The new algorithm constructs the kernels during the learning and estimates the right number in the results. There is no longer a need to define their number of kernels before the learning. This means there is no need to test networks with a number of kernels that is too large, and the number of kernels is no longer a parameter in the selection process (in cross-validation). The results show that the proposed algorithm constructs reasonable kernel bases, and final neural networks are accurate in classification.