Support Vector Machines for Multiple-Instance Learning

Part of Advances in Neural Information Processing Systems 15 (NIPS 2002)

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Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann


This paper presents two new formulations of multiple-instance learning as a maximum margin problem. The proposed extensions of the Support Vector Machine (SVM) learning approach lead to mixed integer quadratic programs that can be solved heuristically. Our generalization of SVMs makes a state-of-the-art classification technique, including non-linear classification via kernels, available to an area that up to now has been largely dominated by special purpose methods. We present experimental results on a pharma(cid:173) ceutical data set and on applications in automated image indexing and document categorization.