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Stuart Andrews

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3 papers

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NeurIPS Conference 2003 Conference Paper

Multiple Instance Learning via Disjunctive Programming Boosting

Stuart Andrews
Thomas Hofmann

Learning from ambiguous training data is highly relevant in many applications. We present a new learning algorithm for classiﬁcation problems where labels are associated with sets of pattern instead of individual patterns. This encompasses multiple instance learn- ing as a special case. Our approach is based on a generalization of linear programming boosting and uses results from disjunctive programming to generate successively stronger linear relaxations of a discrete non-convex problem.

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AAAI Conference 2002 Short Paper

Multiple Instance Learning with Generalized Support Vector Machines

Stuart Andrews
and Ioannis Tsochantaridis

In pattern classification it is usually assumed that a training set of patterns along with their class labels is available. Multiple-Instance Learning (MIL) generalizes this problem setting by making weaker assumptions about the labeling information. We propose to generalize Support Vector Machines to take into account such weak labeling of the type found in MIL. Our method is able to identify superior discriminant functions, as is demonstrated in experiments on synthetic and image datasets.

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NeurIPS Conference 2002 Conference Paper

Support Vector Machines for Multiple-Instance Learning

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.

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