a novel classification technique based on progressive transductive svm learning论文.pdf

Pattern Recognition Letters 42 (2014) 101–106 Contents lists available at ScienceDirect Pattern Recognition Letters journal homepage: www.else /locate/patrec A novel classification technique based on progressive transductive SVM learning q Anshu Singla a, Swarnajyoti Patra b,⇑, Lorenzo Bruzzone c a School of Mathematics and Computer Applications, Thapar University, Patiala 147004, India b Department of Computer Science and Engineering, Tezpur University, Tezpur 784028, India c Department of Information Engineering and Computer Science, University of Trento, Trento 38123, Italy a r t i c l e i n f o a b s t r a c t Article history: The existing semisupervised techniques based on progressive transductive support vector machine Received 11 September 2013 (PTSVM) iteratively select transductive samples that are closest to the SVM margin bounds. This may Available online 15 February 2014 result in selecting wrong patterns (i.e., patterns that when included in the semisupervised learning can be associated with a wrong label) as transductive samples, especially when poor initial training sets Keywords: are available or when available training samples are biased. To mitigate this problem, the proposed Cluster assumption approach considers the distance from SVM margin bounds, the properties of the k-nearest neighbors k-Nearest neighbors approac