9. The non-transitory computer-readable storage medium of claim 7 , wherein the text documents are web pages.
7. A non-transitory computer-readable storage medium storing executable computer program instructions comprising: instructions for storing a taxonomy of hierarchically-arranged categories; instructions for storing a set of labeled videos, each of the labeled videos having associated textual metadata and being initially labeled as representing one or more of the categories; instructions for storing labels initially associated with a set of text documents distinct from the labeled videos, each stored label corresponding to one of the categories and indicating that the associated text document represents the category; instructions for identifying, for each of the categories, a positive training subset of the text documents that represent the category based on their stored labels, and a negative training subset of the text documents that do not represent the category based on their stored labels; instructions for training a set of text-based classifiers based on the positive training subsets and the negative training subsets, each text-based classifier associated with one of the categories and producing, when applied to text, a score providing a measure of how strongly the text represents the associated category; instructions for identifying, for each of the categories, a positive training subset of the labeled videos that represent the category based on their labels, and a negative training subset of the labeled videos that do not represent the category based on their labels; instructions for, for each video of the positive training subsets of the labeled videos and of the negative training subsets of the labeled videos: applying the text-based classifiers to the associated textual metadata of the video, thereby producing a vector of scores for the video, the scores providing measures of how strongly the textual metadata of the video represents the categories associated with the text-based classifiers; extracting a content feature vector from video content of frames of the video; forming a hybrid feature vector comprising the vector of scores and the content feature vector for the video; and instructions for training a set of adapted classifiers based on the hybrid feature vectors of the videos in the positive training subsets of the labeled videos and on the hybrid feature vectors of the videos in the negative training subsets of the labeled videos, each adapted classifier associated with one of the categories and producing, when applied to an unlabeled video, a score providing a measure of how strongly the unlabeled video represents the associated category.