In this paper we present a boosting approach to multiple instance learning. As weak hypotheses we use balls (with respect to various metrics) centered at instances of positive bags. For the co-norm these hypotheses can be modified into hyper-rectangles by a greedy algorithm. Our approach includes a stopping criterion for the algorithm based on estimates for the generalization error. These estimates can also be used to choose a preferable metric and data normalization. Compared to other approaches our algorithm delivers improved or at least competitive results on several multiple instance benchmark data sets. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Auer, P., & Ortner, R. (2004). A boosting approach to multiple instance learning. In Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) (Vol. 3201, pp. 63–74). Springer Verlag. https://doi.org/10.1007/978-3-540-30115-8_9
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