Semi-Supervised Support Vector Machines

We introduce a semi-supervised support vector machine (S3 VM) method. Given a training set of labeled data and a working set of unlabeled data, S3 VM constructs a support vector machine using both the training and working sets. We use S3 VM to solve the transduction problem using overall risk minimization (ORM) posed by Vapnik. The transduction problem is to estimate the value of a classification function at the given points in the working set. This contrasts with the standard inductive learning problem of estimating the classification function at all possible values and then using the fixed function to deduce the classes of the working set data. We propose a general S3 VM model that minimizes both the misclassification error and the function capacity based on all the available data. We show how the S3 VM model for 1-norm linear support vector machines can be converted to a mixed-integer program and then solved exactly using integer programming. Results of S3 VM and the standard 1-nor...

• Type: Conference
• Year: 1998
• Where: NIPS
• Authors: Kristin P. Bennett, Ayhan Demiriz
• Keywords: NIPS 1998, NIPS 2007, S3 VM, S3 VM Model, Support Vector Machine