Support vector data description method for solving multiple instance problems

Support Vector Data Description (SVDD) is introduced into multiple instance learning. Three multi-instance learning methods based on SVDD are presented, which include Multiple Instance Learning based on SVDD and bag classification (mi-SVDD) or instance classification (MI-SVDD), and Multiple Instance Learning based on SVDD and positive instance prediction (SVDD-MILD-I). Experimental results on MUSK dataset show that precisions of mi-SVDD and MI-SVDD are quite comparable to those of mi-SVM and MI-SVM; SVDD-MILD-I can get highest accuracy among all the methods known so far. Experimental results in the application of content based image retrieval in COREL image collections demonstrate that precision achieved by SVDD-MILD_I is higher than the others. Additionally, SVDD-MILD_I discriminates the misclassification-prone images between Beach and Mountains quite well.