Multiple Instance Twin Support Vector Machines
Yuan‐Hai Shao, Zhixia Yang, Xiaobo Wang, Nai-Yang Deng · 2010
Abstract Considering the multiple instance learning(MIL) in classification problem, a novel mul-tiple instance twin support vector machines(MI-TWSVM) method is proposed. For linear classifi-cation, unlike other maximum margin SVM-based MIL methods, the proposed approach leads to two non-parallel hyperplanes. The non-linear classification via kernels is also studied. Prelimi-nary experimental results on public datasets indicate that our MIL method is competitive with the previous MIL methods.