Many approaches to object recognition are founded on probability theory, and can be broadly characterized as either generative or discriminative according to whether or not the dis...
Generative algorithms for learning classifiers use training data to separately estimate a probability model for each class. New items are classified by comparing their probabiliti...
Extracting appropriate custom instructions is an important phase for implementing an application on an extensible processor with a reconfigurable functional unit (RFU). Custom inst...
Sequence compression is one of the most promising tools for strengthening pseudo-random generators used in stream ciphers. Indeed, adding compression components can thwart algebrai...
A serious problem of most transformation-based generators is that they are trying to achieve three mutually antagonistic goals simultaneously: 1) deeply factored operators and oper...