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 problems, 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. Furthermore, 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 problem 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. © 2013 The Authors. Published by Elsevier B.V.
Zhang, Q., Tian, Y., & Liu, D. (2013). Nonparallel support vector machines for multiple-instance learning. In Procedia Computer Science (Vol. 17, pp. 1063–1072). Elsevier B.V. https://doi.org/10.1016/j.procs.2013.05.135