Statistical Approach to Support Vector Machine
Yashima Ahuja, Sumit Yadav · 2013
This paper provides you a brief description about support vector machine. In this, we have explained other universal approximators rather than layered feed-forward network and radial-basis function networks. Here, we have also explained mathematical concepts of support vector machine. Support Vector Machine is a machine learning tool which can be mainly used for classification and regression. It represents complex patterns and excludes bogus patterns. Classification involves grouping entities on the basis of some common properties or attributes. Support Vector machine is a linear machine with several nice properties. The core purpose of support vector machine is to separate the data with decision boundary in linear approach and extend it to non-linear approach by using kernel trick. It is used for many applications such as text categorization, face recognition, pattern recognition and many more. Traditional approaches and techniques perform weakly in case of high dimension data. Support vector machine overcomes all that pitfalls of previous techniques. In this, we have mainly focused on mathematical concepts rather than on theoretical.