Nonparallel Support Vector Machines for Multiple-Instance Learning

Abstract

In this paper, we proposed a new multiple-instance learning (MIL) method based on nonparallel support vector machines (called MI-NPSVM). For the linear case, MI-NPSVM constructs two nonparallel hyperplanes by solving two SVM-type prob- lems, which is different from many other maximum margin SVM-based MIL methods. For the nonlinear case, kernel functions can be easily applied to extend the linear case, which is different from other nonparallel SVM-based MIL methods. Further- more, compared with the existing MIL method based on nonparallel SVM – MI-TSVM, MI-NPSVM has two main advantages. Firstly the method enforces the structural risk minimization; secondly it does not need to solve a bilevel programming prob- lem anymore, but to solve a series of standard Quadratic Programming Problems (QPPs). All experimental results on public datasets show that our method is superior to the traditional MIL methods like MI-SVM, MI-TSVM etc.