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

TL;DR

This paper introduces the Evidence Feed Forward Hidden Markov Model, a novel variant that incorporates observation-to-observation linkages, enhancing classification accuracy for visual human intent and measurement data.

Contribution

It develops the theory and mathematical proofs for Evidence Feed Forward HMMs, demonstrating their superiority over standard HMMs in classification tasks.

Findings

Evidence Feed Forward HMMs learn parameters effectively.

Improved classification accuracy over standard HMMs.

Applicable to visual action and measurement data.

Abstract

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.