P. M. Long and L. Tan. PAC-learning axis alligned rectangles with respect to product distributions from multiple-instance examples. In Proceedings of the 1996 Conference on Computational Learning Theory, 1996. Home/Search Document Not in Database Summary Related Articles Check |
This paper is cited in the following contexts:
A Framework for Multiple-Instance Learning - Maron, Lozano-Pérez (1998) (25 citations) (Correct)
....was the right key. The multiple instance learning model was only recently formalized by [Dietterich et al. 1997] They assume a hypothesis class of axis parallel rectangles, and develop algorithms for dealing with the drug activity prediction problem described above. This work was followed by [Long and Tan, 1996] , where a high degree polynomial PAC bound was given for the number of examples needed to learn in the multiple instance learning model. Auer, 1997] gives a more efficient algorithm, and [Blum and Kalai, 1998] shows that learning from multiple instance examples is reducible to PAC learning with ....
P. M. Long and L. Tan. PAC-learning axis alligned rectangles with respect to product distributions from multiple-instance examples. In Proceedings of the 1996 Conference on Computational Learning Theory, 1996.
Multiple-Instance Learning for Natural Scene Classification - Maron, Ratan (1998) (34 citations) (Correct)
....to positive instances in a positively labeled bag (the noise ratio) can be arbitrarily high. The multiple instance learning model was only recently formalized by [ Dietterich et al. 1997 ] where they develop algorithms for the drug activity prediction problem. This work was followed by [ Long and Tan, 1996, Auer et al. 1996, Blum and Kalai, 1998 ] who showed that it is difficult to PAC learn in the Multiple Instance model unless very restrictive independence assumptions are made about the way in which examples are generated. Auer, 1997 ] shows that despite these assumptions, the MULTINST ....
P. M. Long and L. Tan. PAClearning axis alligned rectangles with respect to product distributions from multiple-instance examples. In Proceedings of the 1996 Conference on Computational Learning Theory, 1996.
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