Online multiple instance learning with no regret
CVPR(2010)
摘要
Multiple instance (MI) learning is a recent learning paradigm that is more flexible than standard supervised learning algorithms in the handling of label ambiguity. It has been used in a wide range of applications including image classification, object detection and object tracking. Typically, MI algorithms are trained in a batch setting in which the whole training set has to be available before training starts. However, in applications such as tracking, the classifier needs to be trained continuously as new frames arrive. Motivated by the empirical success of a batch MI algorithm called MILES, we propose in this paper an online MI learning algorithm that has an efficient online update procedure and also performs joint feature selection and classification as MILES. Besides, while existing online MI algorithms lack theoretical properties, we prove that the proposed online algorithm has a (cumulative) regret of O(√T), where T is the number of iterations. In other words, the average regret goes to zero asymptotically and it thus achieves the same performance as the best solution in hindsight. Experiments on a number of MI classification and object tracking data sets demonstrate encouraging results.
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关键词
online mi learning algorithm,online update procedure,online algorithm,learning (artificial intelligence),batch mi algorithm,miles,object tracking data sets,standard supervised learning algorithms,image classification,tracking,object detection,learning paradigm,label ambiguity,online multiple instance learning,iterative methods,batch setting,bismuth,image segmentation,image reconstruction,semiconductor device modeling,object tracking,surface texture,supervised learning,learning artificial intelligence,surface reconstruction,feature selection,layout,stereo vision,cumulant,accuracy,support vector machines
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