The Planning-ahead SMO Algorithm
This is an incremental improvement to a standard method for SVM training, potentially benefiting machine learning practitioners.
The paper tackles the problem of improving the sequential minimal optimization (SMO) algorithm for SVM training by introducing a planning-ahead modification to enhance step size, resulting in demonstrated superiority in experiments across multiple datasets.
The sequential minimal optimization (SMO) algorithm and variants thereof are the de facto standard method for solving large quadratic programs for support vector machine (SVM) training. In this paper we propose a simple yet powerful modification. The main emphasis is on an algorithm improving the SMO step size by planning-ahead. The theoretical analysis ensures its convergence to the optimum. Experiments involving a large number of datasets were carried out to demonstrate the superiority of the new algorithm.