Abstract: One embodiment of the present invention provides a system that merges similar clusters of conceptually-related words in a probabilistic generative model for textual documents. During operation, the system receives a current model, which contains terminal nodes representing random variables for words and contains cluster nodes representing clusters of conceptually related words. Nodes in the current model are coupled together by weighted links, wherein if a node fires, a link from the node to another node causes the other node to fire with a probability proportionate to the weight of the link. Next, the system determines whether cluster nodes in the current model explain other cluster nodes in the current model. If two cluster nodes explain each other, the system merges the two cluster nodes to form a combined cluster node.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing optical character recognition. In one aspect, a method includes receiving a text image I. A set of feature functions are evaluated for a log linear model to determine respective feature values for the text image I, wherein each feature function hi maps the text image I to a feature value, and wherein each feature function hi is associated with a respective feature weight ?i. A transcription {circumflex over (T)} is determined that minimizes a cost of the log linear model.

Abstract: Systems, methods, and apparatuses including computer program products for machine learning. A method is provided that includes determining model parameters for a plurality of feature functions for a linear machine learning model, ranking the plurality of feature functions according to a quality criterion, and selecting, using the ranking, a group of feature functions from the plurality of feature functions to update with the determined model parameters.

Abstract: Systems, methods, and apparatuses including computer program products for machine learning. A method is provided that includes determining model parameters for a plurality of feature functions for a linear machine learning model, ranking the plurality of feature functions according to a quality criterion, and selecting, using the ranking, a group of feature functions from the plurality of feature functions to update with the determined model parameters.

Abstract: Among other disclosed subject matter, a computer-implemented method includes identifying a first object that belongs to a first domain. The method includes identifying, using the first object, at least a first cluster node in a generative model that includes a plurality of first cluster nodes having weighted relationships to respective ones of a plurality of second objects. The method includes identifying, in response to identifying the first object, at least one of the second objects, the second object belonging to the first domain and being identified using the first cluster node and its respective weighted relationship.