SVM approach for non-parametric method in classification and regression learning process on feature selection with $epsilon$-insensitive region

Abstract

Machine Learning is considered as a subfield of Artificial Intelligence. Machine Learning is concerned with the development of techniques and design the methods which enable the computer to learn. The field of machine learning is concerned with constructing computer program that automatically improve its performance with experience. In today's machine learning applications, support vector machines are considered (SVM) one of the most robust and accurate methods among all well-known algorithms and also being developed at a fast space. The aim of SVM is to find the best classification function, in a two-class learning task, and to distinguish between members of the two classes in the training data. Hence, the goal of machine learning is to find the output hypothesis that performed the correct classification of the training data, but the other earlier algorithms to find the hypothesis that accurate fit to the data. SVM requires that each data instance is represented as a vector of real numbers. Hence, if there are categorical attributes, convert them into numerical data, then we using m numbers to represent an m-category attribute. Only one of the m numbers is one, and others are zero. Machine learning has been applied in various field such as medical diagnosis, bioinformatics, detecting credit card fraud, classifying DNA sequences, speech and handwriting recognition, object recognition in computer vision, and robot locomotion. The objective is algorithmic approach for non parametric methods to tractable for high dimensional massive datasets.