@inproceedings{4138822de3f142199e0196c2918b101a,

title = "Reducing label complexity by learning from bags",

abstract = "We consider a supervised learning setting in which the main cost of learning is the number of training labels and one can obtain a single label for a bag of examples, indicating only if a positive example exists in the bag, as in Multi- Instance Learning. We thus propose to create a training sample of bags, and to use the obtained labels to learn to classify individual examples. We provide a theoretical analysis showing how to select the bag size as a function of the problem parameters, and prove that if the original labels are distributed unevenly, the number of required labels drops considerably when learning from bags. We demonstrate that finding a low-error separating hyperplane from bags is feasible in this setting using a simple iterative procedure similar to latent SVM. Experiments on synthetic and real data sets demonstrate the success of the approach.",

author = "Sivan Sabato and Nathan Srebro and Naftali Tishby",

year = "2010",

language = "???core.languages.en_GB???",

volume = "9",

series = "Journal of Machine Learning Research",

publisher = "Microtome Publishing",

pages = "685--692",

booktitle = "Proceedings of the 13th International Conference on Artificial Intelligence and Statistics (AISTATS)",

note = "null ; Conference date: 13-05-2010 Through 15-05-2010",

}