An Online Gradient Method with Smoothing L_0 Regularization for Pi-Sigma Network
Khidir Shaib Mohamed, Yousif Shoaib Mohammed · Transactions on Machine Learning and Artificial Intelligence · 2018
The description of this study is to make possibility analysis solution of online gradient method with smoothing regularization for pi-sigma network training. Due to the effectiveness computational and theoretical analysis are a very important issues to improve the generalization performance of networks and the gradient descent algorithm with regularization is widely used method. However, regularization is reefed to NP-hard nature problems, which has not differentiable objective functional-penalty term. In this paper to avoid this trick, we use a smoothing function to recover the origin regularization into smoothing regularization. Under this condition, the resulting obtained as a good decreases solution when compared with others. The monotonically of the error function, weak and strong convergence theorems are proved.