Data Classification using Support Vector Machines

Classifying data is a common task in machine learning. In machine learning, statistical classification is the problem of identifying the sub-population to which new observations belong on the basis of a training set of data containing observations whose sub-population is known. Therefore these classifications will show a variable behavior which can be studied by statistics. In machine learning, the classification problem is known as supervised learning, while clustering is known as unsupervised learning. SVM’s analyze data and recognize patterns with support vector methods. This paper is a comprehensive study of SVM fundamentals, research and how it is used for anomaly detection. The paper discusses the mathematical modeling on which SVM is based on. We also discuss tools and algorithms which can be used to perform classification using SVM.