Volume : IV, Issue : IV, April - 2014

Learning Algorithm of Svm Reduce The Optimization Error And Give The Maximum Accuracy of The QP

M. Premalatha, Dr. C. Vijayalakshmi

Abstract :

 The field of machine learning is concerned with constructing computer program that automatically improve its performance with experience. SVMs (Support Vector Machines) are a useful technique for data classification. Support Vector Machine (SVM) is a linear machine working in the highly dimensional feature space formed by the nonlinear mapping of the N–dimensional input vector x into a K–dimensional feature space (K>N) through the use of a mapping Φ (x). The data points corresponding to the non–zero weights are called support vectors. The main goal is to measure the error to get the exact solution can be approximated by a function and also get the error accurately to determine the best function implemented by learning system using finite training set and testing set (unseen). The best function closely measure the optimization error in finite training set then the function have less approximation to lead a large estimation error. The main goal of learning algorithm is minimize the training set or time. Smaller constraint by the number of training data, the error is dominated by the approximation then the optimization error can be reduced the iterative time.

 

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Article: Download PDF   DOI : 10.36106/ijar  

Cite This Article:

"M.Premalatha, Dr.C.Vijayalakshmi Learning Algorithm of Svm Reduce The Optimization Error And Give The Maximum Accuracy of The QP Indian Journal of Applied Research, Vol.IV, Issue. IV

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