Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, micro-array gene expression, proteins structure predictions, data Classification etc. Most of the existing supervised classification methods are based on traditional statistics, which can provide ideal results when sample size is tending to infinity. However, only finite samples can be acquired in practice. In this paper, a novel learning method, Support Vector Machine (SVM), is applied on different data (Diabetes data, Heart Data, Satellite Data and Shuttle data) which have two or multi class. SVM, a powerful machine method developed from statistical learning and has made significant achievement in some field. Introduced in the early 90’s, they led to an explosion of interest in machine learning. The foundations of SVM have been developed by Vapnik and are gaining popularity in field of machine learning due to many attractive features and promising empirical performance. SVM method does not suffer the limitations of data dimensionality and limited samples [1] & [2].
In our experiment, the support vectors, which are critical for classification, are obtained by learning from the training samples. In this paper we have shown the comparative results using different kernel functions for all data samples.