抄録
Integration of existing information accumulated in individuals, such as experience and knowledge, is effective for turning private judgments of various individuals into the collective decision. Such effective use of existing information will be a good reference for the development of dependable intelligent information systems that can cope with unfamiliar situation. This paper considers a representation model for the information integration from the view point of information theory. The representation model regards the manner of information integration as a mixture of probability distributions under the assumption that they characterize the structures of existing information sources. The mixture of probability distributions is designed through information divergence measures such as Kullback-divergnence and alpha-divergence. The designed mixture of probability distributions includes not only conventional linear type but also exponential and power types. The present paper also considers the relationship between the above mentioned representation model of information integration and so called ensemble learning.
