INFORMATION BOTTLENECKS, CAUSAL STATES, AND STATISTICAL RELEVANCE BASES: HOW TO REPRESENT RELEVANT INFORMATION IN MEMORYLESS TRANSDUCTION
نویسندگان
چکیده
منابع مشابه
Information Bottlenecks, Causal States, and Statistical Relevance Bases: How to Represent Relevant Information in memoryless transduction
Recently, Tishby, Pereira, and Bialek proposed a new method for finding concise representations of the information one set of variables contains about another [1]. This brief note explains the connections between this “information-bottleneck” method and existing mathematical frameworks and techniques. This comparison should enhance the value of research in this promising direction and clarify t...
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ژورنال
عنوان ژورنال: Advances in Complex Systems
سال: 2002
ISSN: 0219-5259,1793-6802
DOI: 10.1142/s0219525902000481