13. A non-transitory computer-readable storage medium having executable computer program instructions embodied therein for forming a unified classifier for a category, actions of the computer program instructions comprising: storing a taxonomy of categories, the categories for labeling videos; accessing a video repository; retrieving from the video repository a training set of videos for a category from the taxonomy of categories, the training set comprising a positive training set of videos labeled with the category, and a negative training set of videos not labeled with the category; forming a plurality of positive bags, each positive bag comprising a plurality of the videos from the positive training set; forming a plurality of negative bags, each negative bag comprising a plurality of the videos from the negative training set; applying a boosting algorithm to obtain a plurality of sets of weights, wherein each set of weights comprises one weight for each bag, the weight for each bag based on a weight of each video in the plurality of videos in the bag; training a unified classifier, wherein training the unified classifier is based at least in part on the plurality of positive bags, the plurality of negative bags, and the plurality of sets of weights; and storing the unified classifier.
13. A non-transitory computer-readable storage medium having executable computer program instructions embodied therein for forming a unified classifier for a category, actions of the computer program instructions comprising: storing a taxonomy of categories, the categories for labeling videos; accessing a video repository; retrieving from the video repository a training set of videos for a category from the taxonomy of categories, the training set comprising a positive training set of videos labeled with the category, and a negative training set of videos not labeled with the category; forming a plurality of positive bags, each positive bag comprising a plurality of the videos from the positive training set; forming a plurality of negative bags, each negative bag comprising a plurality of the videos from the negative training set; applying a boosting algorithm to obtain a plurality of sets of weights, wherein each set of weights comprises one weight for each bag, the weight for each bag based on a weight of each video in the plurality of videos in the bag; training a unified classifier, wherein training the unified classifier is based at least in part on the plurality of positive bags, the plurality of negative bags, and the plurality of sets of weights; and storing the unified classifier. 15. The non-transitory computer-readable medium of claim 13 , wherein each video is associated with a feature vector determined by a set of initial classifiers, and training the unified classifier based on the plurality of positive bags and plurality of negative bags comprises training the unified classifier based on the feature vectors associated with the videos in the plurality of positive bags and the plurality of negative bags.