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Evidence Feed Forward Hidden Markov Model: A New Type Of Hidden Markov Model

ISSN : 0975-900X
Journal from gdlhub / 2017-08-14 11:52:33
Oleh : Michael Del Rose , Christian Wagner , and Philip Frederick, International Journal of Artificial Intelligence & Applications
Dibuat : 2012-06-25, dengan 1 file

Keyword : Hidden Markov Model, Visual Human Intent Analysis, Visual Understanding, Image Processing, Artificial Intelligence
Subjek : Evidence Feed Forward Hidden Markov Model: A New Type Of Hidden Markov Model
Url : http://airccse.org/journal/ijaia/papers/0111ijaia01.pdf
Sumber pengambilan dokumen : Internet

The ability to predict the intentions of people based solely on their visual actions is a skill only performed


by humans and animals. The intelligence of current computer algorithms has not reached this level of


complexity, but there are several research efforts that are working towards it. With the number of


classification algorithms available, it is hard to determine which algorithm works best for a particular


situation. In classification of visual human intent data, Hidden Markov Models (HMM), and their


variants, are leading candidates.


The inability of HMMs to provide a probability in the observation to observation linkages is a big


downfall 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 to


summarize the actions, and thus determine, with pretty good accuracy, the intention of the person


performing the action. These visual cues and linkages are important in creating intelligent algorithms


for determining human actions based on visual observations.


The Evidence Feed Forward Hidden Markov Model is a newly developed algorithm which provides


observation to observation linkages. The following research addresses the theory behind Evidence Feed


Forward HMMs, provides mathematical proofs of their learning of these parameters to optimize the


likelihood of observations with a Evidence Feed Forwards HMM, which is important in all


computational intelligence algorithm, and gives comparative examples with standard HMMs in


classification of both visual action data and measurement data; thus providing a strong base for


Evidence Feed Forward HMMs in classification of many types of problems.

Deskripsi Alternatif :

The ability to predict the intentions of people based solely on their visual actions is a skill only performed


by humans and animals. The intelligence of current computer algorithms has not reached this level of


complexity, but there are several research efforts that are working towards it. With the number of


classification algorithms available, it is hard to determine which algorithm works best for a particular


situation. In classification of visual human intent data, Hidden Markov Models (HMM), and their


variants, are leading candidates.


The inability of HMMs to provide a probability in the observation to observation linkages is a big


downfall 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 to


summarize the actions, and thus determine, with pretty good accuracy, the intention of the person


performing the action. These visual cues and linkages are important in creating intelligent algorithms


for determining human actions based on visual observations.


The Evidence Feed Forward Hidden Markov Model is a newly developed algorithm which provides


observation to observation linkages. The following research addresses the theory behind Evidence Feed


Forward HMMs, provides mathematical proofs of their learning of these parameters to optimize the


likelihood of observations with a Evidence Feed Forwards HMM, which is important in all


computational intelligence algorithm, and gives comparative examples with standard HMMs in


classification of both visual action data and measurement data; thus providing a strong base for


Evidence Feed Forward HMMs in classification of many types of problems.

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