An elaboration of sequential minimal optimization for support vector regression

Abstract

The computational reduction by sequential minimal optimization (SMO) is crucial for support vector regression (SVR) with large-scale function approximation. Due to the importance, the paper surveys broadly the relevant researches, digests their essentials, and then reorganizes the theory with a plain explanation. Sought first to provide a literal comprehension of SVR-SMO, the paper reforms the mathematical development with a framework of unified and non-interrupted derivations together with appropriate illustrations to visually clarify the key ideas. The development is also examined by an alternative viewpoint. The cross-examination achieves the foundation of the development more solid, and leads to a consistent suggestion of a straightforward generalized algorithm. Some consistent experimental results are also included.