Publication

Abstract : Classification is one of the main stream areas in machine learning. Many a time we have to deal with data sets that are not classifiable using a linear separator. This paper presents a theoretical and an experimental study of random kitchen sink algorithm which makes it possible for such data sets to become linearly separable by efficiently, effectively and explicitly mapping it to an appropriate higher dimensional space. It explores the dependency of various parameters of the algorithm, mainly the dimension and variance of the concerned random variable on the accuracy of classification, and how to fine tune these parameters to obtain the best of results. It also presents an intuitive understanding of how these parameters actually affect the accuracy of classification by connecting it with the shift invariant RBF kernel. The effect of choice of these parameters on a two class classification problem is also included. © Research India Publicatio