Evaluating the Performance of an Admissible Kernel Function in Banach Space for Binary Data

Classification is a learning function that maps a given data item into one of several predefined classes. It is a data analysis technique that extracts models describing important data classes and predicts future values. Basically, classification techniques have better capability to handle a wider variety of datasets than regression. It can also be described as a supervised learning algorithm in the machine learning process. Support Vector Machine (SVM) is an emerging classifier based on supervised machine learning approach. It is originally used to symbolize popular and modern classifiers that have a well-defined theoretical foundation to provide some enviable performances [1]. In this paper, an admissible kernel function in Banach Space is proposed as an optimal kernel function for real time applications. The experimental results are carried out using benchmark binary datasets which are taken from UCI Machine Learning Repository and their performance are evaluated using various measures like support vector, support vector percentage, training time and accuracy.