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    【讲座预告】Linkage Discovery in Data Analytics:Bidirectional and Multidirectional Associative Memories and Information


    Linkage Discovery in Data Analytics:

    Bidirectional and Multidirectional Associative Memories

    and  Information Granules




    Associative memories are representative examples of associative structures, which have been studied intensively in the literature and have resulted in a plethora of applications in areas of control, classification, and data analysis. The underlying idea is to realize associative mapping so that the recall processes (both one-directional and bidirectional) are characterized by a minimal recall error.

    We revisit and augment the concept of associative memories by proposing some new design directions. We focus on the essence of structural dependencies in the data and make the corresponding associative mappings spanned over a related collection of landmarks (prototypes). We show that a construction of such landmarks is supported by mechanisms of collaborative fuzzy clustering. A logic-based characterization of the developed associations established in the framework of relational computing is discussed as well.

    Furthermore we generalize associative mappings into their granular counterparts in which the originally formed numeric prototypes are made granular so that the quality of the associative recall can be quantified. Several scenarios of allocation of information granularity aimed at the optimization of the characteristics of recalled results (information granules) quantified in terms of coverage and specificity criteria are proposed.

    Structural augmentations of the discussed architectures to multisource and multi-directional memories involving associative mappings among various data spaces are proposed and their design is discussed. Ways of incorporating  mechanisms of deep learning mechanisms are also studied.



    Witold Pedrycz教授是加拿大皇家科学院院士,加拿大工程院院士,波兰科学院外籍院士,现任加拿大阿尔伯塔大学(University of Alberta)计算机智能研究中心主席,英国诺丁汉大学计算机科学学院特聘教授,国际模糊系统联合会(IFSA)和国际电气电子工程师学会(IEEE)会士(Fellow)。曾任国际模糊系统联合会(IFSA)和北美模糊系统协会(NAFES)主席,现担任著名国际期刊Information Sciences的主编,是IEEE Transactionson Fuzzy Systems等多个国际知名期刊的副主编。2007年获得Norbert Wiener award (IEEE Systems, Man, and Cybernetics Society),该奖是IEEE SMCC协会颁发的最高技术成就奖,2009年获得Soft Computing领域国际最高奖Cajastur Prize,还获得IEEE加拿大计算机工程勋章,2013年获得加拿大皇家科学学会最高奖Killam Prize。Witold Pedrycz教授长期从事人工智能、模糊系统以及数据挖掘等相关领域研究,发表SCI高水平论文900多篇,出版专著15部,H指数为90,为计算智能、粒计算、数据挖掘和不确定性系统建模的研究做出了重要贡献,研究工作得到了世界范围内同行的广泛关注和认可。