Single and Union Non-parallel Support Vector Machine Frameworks
This work addresses classification challenges for machine learning practitioners, but it is incremental as it builds on existing nonparallel support vector machine methods.
The authors tackled the classification problem by summarizing nonparallel support vector machines into two frameworks: one constructing hyperplanes separately and the other simultaneously, and introduced NSVM, a max-min distance-based method for multiclass classification that showed advantages in experiments on benchmark datasets.
Considering the classification problem, we summarize the nonparallel support vector machines with the nonparallel hyperplanes to two types of frameworks. The first type constructs the hyperplanes separately. It solves a series of small optimization problems to obtain a series of hyperplanes, but is hard to measure the loss of each sample. The other type constructs all the hyperplanes simultaneously, and it solves one big optimization problem with the ascertained loss of each sample. We give the characteristics of each framework and compare them carefully. In addition, based on the second framework, we construct a max-min distance-based nonparallel support vector machine for multiclass classification problem, called NSVM. It constructs hyperplanes with large distance margin by solving an optimization problem. Experimental results on benchmark data sets show the advantages of our NSVM.