EVIDENCE FEED FORWARD HIDDEN MARKOV MODEL: A NEW TYPE OF HIDDEN MARKOV MODEL

The ability to predict the intentions of people based solely on their visual actions is a skill only performedby humans and animals. The intelligence of current computer algorithms has not reached this level ofcomplexity, but there are several research efforts that are working towards it. With the number ofclassification algorithms available, it is hard to determine which algorithm works best for a particularsituation. In classification of visual human intent data, Hidden Markov Models (HMM), and theirvariants, are leading candidates. The inability of HMMs to provide a probability in the observation to observation linkages is a bigdownfall in this classification technique. If a person is visually identifying an action of another person, they monitor patterns in the observations. By estimating the next observation, people have the ability tosummarize the actions, and thus determine, with pretty good accuracy, the intention of the personperforming the action. These visual cues and linkages are important in creating intelligent algorithmsfor determining human actions based on visual observations. The Evidence Feed Forward Hidden Markov Model is a newly developed algorithm which providesobservation to observation linkages. The following research addresses the theory behind Evidence FeedForward HMMs, provides mathematical proofs of their learning of these parameters to optimize thelikelihood of observations with a Evidence Feed Forwards HMM, which is important in allcomputational intelligence algorithm, and gives comparative examples with standard HMMs inclassification of both visual action data and measurement data; thus providing a strong base forEvidence Feed Forward HMMs in classification of many types of problems