A Study on Support Vector Machine based Linear and Non-Linear Pattern Classification 论文

摘要

The best way to acquire knowledge about an algorithm is feeding it data and checking the result. In a layman's language machine learning can be called as an ideological child or evolution of the idea of understanding algorithm through data. Machine learning can be subdivided into two paradigms, supervised learning and unsupervised learning. Supervised learning is implemented to classify data using algorithms like support vector machines (SVM), linear regression, logistic regression, neural networks, nearest neighbor etc. Supervised learning algorithm uses the concepts of classification and regression. Linear classification was earlier used to form the decision plane but was bidimensional. But a particular dataset might have required a non linear decision plane. This gave the idea of the support vector machine algorithm which can be used to generate a non linear decision boundary using the kernel function. SVM is a vast concept and can be implemented on various real world problems like face detection, handwriting detection and many more. This paper surveys the various concepts of support vector machines, some of its real life applications and future aspects of SVM.