Data Mining via Support Vector Machines

Abstract

Support vector machines (SVMs) have played a key role in broad classes of problems arising in various elds. Much more recently, SVMs have become the tool of choice for problems arising in data classi - cation and mining. This paper emphasizes some recent developments that the author and his colleagues have contributed to such as: gen- eralized SVMs (a very general mathematical programming framework for SVMs), smooth SVMs (a smooth nonlinear equation representation of SVMs solvable by a fast Newton method), Lagrangian SVMs (an unconstrained Lagrangian representation of SVMs leading to an ex- tremely simple iterative scheme capable of solving classi cation prob- lems with millions of points) and reduced SVMs (a rectangular kernel classi er that utilizes as little as 1% of the data).