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. 6. The method of claim 1 , wherein each of the authoritatively labeled videos is authoritatively labeled by a human category expert.