nips nips2002 nips2002-192 nips2002-192-reference knowledge-graph by maker-knowledge-mining
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Author: Stuart Andrews, Ioannis Tsochantaridis, Thomas Hofmann
Abstract: 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 pharmaceutical data set and on applications in automated image indexing and document categorization. 1