Support Vector Approach for Classification and Regression problems in Misclassified Data produce sparse solution

M. Premalatha & Dr. C. Vijayal

Abstract

Machine Learning is considered as a subfield of Artificial Intelligence and it is concerned with the development of techniques and methods which enable the computer to learn. Hence, the goal of learning was to output a hypothesis that performed the correct classification of the training data and early learning algorithms were designed to find such an accurate fit to the data. Since then SVMs have been successfully applied to real-world data analysis problems, often providing improved results compared with other techniques. It gives the clear idea for the advantage of the support vector approach is that sparse solutions to classification and regression problems in misclassified data. This fact facilitates the application of SVMs to problems that involve a large amount of data.