Randomly Selection of Interior Points in SV Learning Algorithm Uses of Confidence Parameter

Abstract

Machine learning is a one of the subsets of AI. When a data received in the training set, according to the training dataset to build an algorithmic model based on machine learning concept, it is a specific testable prediction with progressive approach of input and output data that design can be applicable for large dataset in the real-time approach. Support vector machine (SVM) design is in large data applications. In sixties, Russia developed the algorithm based on nonlinear SV which is called generalized portrait algorithm. We evaluate the recognition accuracy of classification of the event using portrait algorithm. The data dependent and data independent show the effectiveness of a support vector machine learning algorithm approach of non-parametric methods to tractable for massive datasets in feature of high dimensional. In this approach, the new set of points, generated to the probability distribution P_{Y((y, f(y)))}. Y((y. f(y))), represented in the example is a support vector, and the probability distribution controls the marginal value and also corresponding weighted vector value. These weighted vectors belong to the training set but not classified with confidence factor. The active SVM learning based on points and their distance between points and hyperplane and newly enters the confidence factor is known as adaptive factor. The confidence factor value is computed from the availability of information around us by using the principle of the k-nearest neighbor, N dimensional data as a input X in to K dimensional space (feature), then K always greater than N through the mapping function ϕ. SVM algorithm gives both computation efficiency and accuracy in a simple manner. In large date set, we can solve by predictor–corrector method in step by step in this process, the similar dates are combined together and give maximum accuracy and also optimum solution to reduce the training error and test error through probability distribution. Here, SVM gives the efficient idea based for function estimation that covers both dependent and independent data.