Implementation Of User Define Kernel For Non-Linear Data Classification In Support Vector Machine (International Journal of Engineering and Computer Science, 2018)
Data Mining is knowledge discovery process in database designed to extract data from a dataset and transforms it in to desired data. data processing action is similarly acclimated in get of constant patterns and/or analytical relationships amid variables, and a new to validate the accusation by applying the detected patterns to new subsets of knowledge.Data categoryification is one in every of the info mining technique to map great amount of data set in to applicable class. Data categoryification is reasonably supervised learning that is employed to predict class for information input, wherever categories are predefined.Supervised learning is that part of automatic learning which focuses on modeling input/output relationship the goal of supervised learning is to identify an optimal mapping from input variables to some output variables, which is based on a sample of observations of the values of the variables. Data classification technique includes various application like handwriting recognition, speech recognition, iris matching, text classification, computer vision, drug design etc. objective of this paper is to survey major techniques of data classification. Several major classification techniques are Artificial neural network, decision trees, k-nearest neighbor(KNN), support vector machine, navie-bayesian classifier, etc This paper introduces a user define kernel technique in svm for data classification, which is applicable to general data including, in particular, imagery and other types of high-dimensional data. By using kernel techniques the framework can account for nonlinearity in the input space.