Support vector machine using efficient instant selection for micro array data sets

Supervised leaning classifier is usually constructing based on models through learning to achieve high accuracy. Support Vector Machine (SVM) is more useful classification technique in supervised learning model. In this paper, we examined SVM with linear kernel function and pre-computed kernel function using micro array data sets. In this observation is focused major three aspects such as accuracy, iteration and support vectors. These datasets are received from UCI machine learning repository. Pre-computed kernel support vector machine has shown best accuracy and minimum execution time to select instances in all data sets in our experiment.