Incremental Support Vector Machine Classification
Glenn Fung, Olvi L. Mangasarian · 2002
Using a recently introduced proximal support vector machine classifier [4], a very fast and simple incremental support vector machine (SVM) classifier is proposed which is capable of modifying an existing linear classifier 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 effectiveness of the algorithm we classify a dataset of 1 billion points in 10-dimensional input space into two classes in less than 2.5 hours on a 400 MHz Pentium II processor.