1 research outputs found
Single Versus Union: Non-parallel Support Vector Machine Frameworks
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 and human face databases show the advantages of our NSVM