1. A computer implemented method of training classifiers for video categories, the method comprising: storing a set of categories as a graph of category nodes, each node corresponding to one of the categories; accessing a first set of training items comprising labeled videos authoritatively labeled as representing one or more of the categories; accessing a second set of unlabeled videos not authoritatively labeled; automatically labeling ones of the unlabeled videos responsive to an observed relationship between ones of the labeled videos and ones of the second set, thereby producing a supplemental set of training items, the supplemental set of training items comprising a plurality of subsets, each subset identified using a different technique, and wherein each of a plurality of the categories has, for each of the plurality of subsets, at least one associated initial classifier trained from the subset; forming a training set comprising the first set and the supplemental set; training based on the training set, for each of a plurality of the categories, an initial classifier associated with the category that when applied to a video produces a score measuring how strongly the video represents the category, thereby producing a set of initial classifiers; training, for each of the plurality of the categories, a unified classifier based at least in part on scores obtained by applying the initial classifiers to the training set, the training using a graph-based statistical learning algorithm that relates scores associated with neighboring nodes of the category graph, the unified classifier when applied to a video producing a score measuring how strongly the video represents the category associated with the unified classifier; and storing the unified classifiers.
1. A computer implemented method of training classifiers for video categories, the method comprising: storing a set of categories as a graph of category nodes, each node corresponding to one of the categories; accessing a first set of training items comprising labeled videos authoritatively labeled as representing one or more of the categories; accessing a second set of unlabeled videos not authoritatively labeled; automatically labeling ones of the unlabeled videos responsive to an observed relationship between ones of the labeled videos and ones of the second set, thereby producing a supplemental set of training items, the supplemental set of training items comprising a plurality of subsets, each subset identified using a different technique, and wherein each of a plurality of the categories has, for each of the plurality of subsets, at least one associated initial classifier trained from the subset; forming a training set comprising the first set and the supplemental set; training based on the training set, for each of a plurality of the categories, an initial classifier associated with the category that when applied to a video produces a score measuring how strongly the video represents the category, thereby producing a set of initial classifiers; training, for each of the plurality of the categories, a unified classifier based at least in part on scores obtained by applying the initial classifiers to the training set, the training using a graph-based statistical learning algorithm that relates scores associated with neighboring nodes of the category graph, the unified classifier when applied to a video producing a score measuring how strongly the video represents the category associated with the unified classifier; and storing the unified classifiers. 4. The method of claim 1 , wherein automatically labeling the supplemental set of training items comprises copying labels from the ones of the labeled videos to the ones of the second set with which the ones of the labeled videos have the observed relationship.