Incremental Support Vector Machine Classi cation

Abstract

Using a recently introduced proximal support vector ma- chine classi er [4], a very fast and simple incremental support vector machine (SVM) classi er is proposed which is capable of modifying an existing linear classi er by both retiring old data and adding new data. A very important feature of the proposed single-pass algorithm , which allows it to handle massive datasets, is that huge blocks of data, say of the order of millions of points, can be stored in blocks of size (n + 1)2, where n is the usually small (typically less than 100) dimensional input space in which the data resides. To demonstrate the e ectiveness of the algorithm we classify a dataset of 1 billion points in 10-dimensional input space into two