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Paper abstract

Improving Classification with Pairwise Constraints: A Margin-based Approach

Nam Nguyen - Cornell University, USA
Rich Caruana - Cornell University, USA

Session: Support Vector Machines
Springer Link: http://dx.doi.org/10.1007/978-3-540-87481-2_8

In this paper, we address the semi-supervised learning problem when there is a small amount of labeled data augmented with pairwise constraints indicating whether a pair of examples belongs to a same class or different classes. We introduce a discriminative learning approach that incorporates pairwise constraints into the conventional margin-based learning framework. We also present an efficient algorithm, PCSVM, to solve the pairwise constraint learning problem. Experiments with 15 data sets show that pairwise constraint information significantly increases the performance of classification.