证据前馈隐马尔可夫模型：一种新型隐马尔可夫模型
Evidence Feed Forward Hidden Markov Model: A New Type of Hidden Markov Model
AI总结：
针对隐马尔可夫模型无法建模观测间关联的问题，提出证据前馈隐马尔可夫模型，通过引入观测间概率链接提升分类性能，并在视觉动作和测量数据上验证其有效性。
摘要：
仅基于视觉动作预测他人意图的能力是人类和动物独有的技能。当前计算机算法的智能尚未达到这种复杂程度，但已有若干研究正朝此方向努力。由于可用的分类算法众多，难以确定哪种算法最适合特定情境。在视觉人类意图数据分类中，隐马尔可夫模型（HMM）及其变体是主要候选方法。HMM无法提供观测间链接的概率，这是该分类技术的一大缺陷。当人通过视觉识别他人的动作时，会监控观测中的模式。通过估计下一个观测，人们能够总结动作，从而相当准确地判断执行动作者的意图。这些视觉线索和链接对于创建基于视觉观测确定人类动作的智能算法至关重要。证据前馈隐马尔可夫模型是一种新开发的算法，它提供了观测间链接。本研究阐述了证据前馈HMM背后的理论，提供了其学习这些参数以优化观测似然性的数学证明（这对所有计算智能算法都至关重要），并给出了与标准HMM在视觉动作数据和测量数据分类中的比较示例，从而为证据前馈HMM在多种问题分类中的应用奠定了坚实基础。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
