Single Versus Union: Non-parallel Support Vector Machine Frameworks

10/22/2019
by   Chun-Na Li, et al.
0

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
11/16/2016

Algebraic multigrid support vector machines

The support vector machine is a flexible optimization-based technique wi...
research
02/22/2020

Longitudinal Support Vector Machines for High Dimensional Time Series

We consider the problem of learning a classifier from observed functiona...
research
11/06/2020

Sequential Minimal Optimization for One-Class Slab Support Vector Machine

One Class Slab Support Vector Machines (OCSSVM) have turned out to be be...
research
04/26/2019

Weighted second-order cone programming twin support vector machine for imbalanced data classification

We propose a method of using a Weighted second-order cone programming tw...
research
07/15/2022

Support Vector Machines with the Hard-Margin Loss: Optimal Training via Combinatorial Benders' Cuts

The classical hinge-loss support vector machines (SVMs) model is sensiti...
research
06/09/2023

Robust Twin Parametric Margin Support Vector Machine for Multiclass Classification

In this paper we present a Twin Parametric-Margin Support Vector Machine...
research
08/01/2017

Learning Deep Convolutional Embeddings for Face Representation Using Joint Sample- and Set-based Supervision

In this work, we investigate several methods and strategies to learn dee...

Please sign up or login with your details

Forgot password? Click here to reset