Vision - Based Hand - Gesture Applications Against Cyberterrorism Finding

Probabilistic models of sequences play a central role inmost machine translation, automated speech recognition,lossless compression, spell-checking, and gene identifica-tion applications to name but a few. Unfortunately, real-world sequence data often exhibit long range dependencieswhich can only be captured by computationally challeng-ing, complex models. Sequence data arising from naturalprocesses also often exhibits power-law properties, yet com-mon sequence models do not capture such properties. Thesequence memoizer is a new hierarchical Bayesian modelfor discrete sequence data that captures long range depen-dencies and power-law characteristics, while remainingcomputationally attractive. Its utility as a language modeland general purpose lossless compressor is demonstrated.