Support Vector Machine

Integrated series on information systems/Integrated series in information systemsPublished 20 October 2015

Shan Suthaharan

Citations1,514

TL;DR

This chapter simplifies the Lagrangian support vector machine approach using process diagrams and data flow diagrams to help readers understand theory and implement it successfully.

Abstract

Support Vector Machine is one of the classical machine learning techniques that can still help solve big data classification problems. Especially, it can help the multidomain applications in a big data environment. However, the support vector machine is mathematically complex and computationally expensive. The main objective of this chapter is to simplify this approach using process diagrams and data flow diagrams to help readers understand theory and implement it successfully. To achieve this objective, the chapter is divided into three parts: (1) modeling of a linear support vector machine; (2) modeling of a nonlinear support vector machine; and (3) Lagrangian support vector machine algorithm and its implementations. The Lagrangian support vector machine with simple examples is also implemented using the R programming platform on Hadoop and non-Hadoop systems.

Keywords

Computer Science

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