title: Twin Support Vector Machines for pattern classification.
abstract: We propose Twin SVM, a binary SVM classifier that determines two nonparallel planes by solving two related SVM-type problems, each of which is smaller than in a conventional SVM. The Twin SVM formulation is in the spirit of proximal SVMs via generalized eigenvalues. On several benchmark data sets, Twin SVM is not only fast, but shows good generalization. Twin SVM is also useful for automatically discovering two-dimensional projections of the data.
authors.affiliation: Department of Electrical Engineering, Indian Institute of Technology, Hauz-Khas, New Delhi, India. jayadeva@ee.iitd.ac.in
authors.lastname: Jayadeva
authors.forename: R
authors.initials: R
authors.lastname: Khemchandani
authors.forename: Suresh
authors.initials: S
authors.lastname: Chandra
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D016000:Cluster Analysis; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*