Non-integer norm regularization SVM via Legendre–Fenchel duality

Support vector machine is an effective classification and regression method that uses VC theory of large margin to maximize the predictive accuracy while avoiding over-fitting of data. L2-norm regularization has been commonly used. If the training data set contains many noise features, L1-norm regularization SVM will provide a better performance. However, both L1-norm and L2-norm are not the optimal regularization method when handling a large number of redundant features and only a small amount of data points are useful for machine learning. We have therefore proposed an adaptive learning algorithm using the p-norm regularization SVM for 0