松尾 徳朗

Generation of useful variables and features is an important issue throughout the machine learning, artificial intelligence, and applied fields for their efficient computations. In this paper, the nearest neighbor relations are proposed for the minimal generation and the reduced variables of the functions in the threshold networks. First, the nearest neighbor relations are shown to be minimal and inherited for threshold functions and they play an important role in the iterative generation of the Chow parameters. Further, they give a solution for the Chow parameters problem. Second, convex cones are made of the nearest neighbor relations for the generation of the reduced variables. Then the edges of convex cones are compared for the discrimination of variables. Finally, the reduced variables based on the nearest neighbor relations are shown to be useful for documents classification.

The development of news media and social media has radically changed the way the public consumes information. This study explores the structure of online news media networks in three countries, namely Indonesia, Malaysia and Singapore, to investigate the phenomenon of fragmentation in the news consumption pattern on social media. Based on the results of the three network indicators used in this study, it can be concluded that the structure of online news media networks in Indonesia and Malaysia shows a tendency of fragmentation. In contrast, this study did not find sufficient evidence that the phenomenon of fragmentation was occurring in the Singapore media network. In-depth analysis on each formed media cluster shows that online news media in Indonesia and Malaysia tend to group based on similarity in market segments, regions or political alignments.