Advantage and drawback of support vector machine functionality

Abstract

Support Vector Machine(SVM)is one of the most efficient machine learning algorithms, which is mostly used for pattern recognition since its introduction in 1990s. SVMs vast variety of usage, such as face and speech recognition, face detection and image recognition has turned it into a very useful algorithm. This has also been applied to many pattern classification problems such as image recognition, speech recognition, text categorization, face detection, and faulty card detection.Statistics was collected from journals and electronic sources published in the period of 2000 to 2013. Pattern recognition aims to classify data based on either a priori knowledge or statistical information extracted from raw data, which is a powerful tool in data separation in many disciplines. The Support Vector Machine (SVM) is a kind of algorithms in biometrics. It is a statistics technical and used orthogonal transformation to convert a set of observations of possibly correlated variables into a set of values of linearly uncorrelated variables.