  In this paper, a straightforward enhancement learning algorithm based on
Separation Index (SI) concept is proposed for Convolutional Neural Networks
(CNNs). At first, the SI as a supervised complexity measure is explained its
usage in better learning of CNNs for classification problems illustrate. Then,
a learning strategy proposes through which the first layer of a CNN is
optimized by maximizing the SI, and the further layers are trained through the
backpropagation algorithm to learn further layers. In order to maximize the SI
at the first layer, A variant of ranking loss is optimized by using the quasi
least square error technique. Applying such a learning strategy to some known
CNNs and datasets, its enhancement impact in almost all cases is demonstrated.
