which many methods have been proposed (e.g., AIC, BIC, MDL, etc.), and these are frequently considered under the rubric of "model regularization". Different interpretations of granular computing Granular computing can be conceived as a framework of theories, methodologies, techniques, and tools that make use of information granules in the process of problem solving. In this sense, granular computing is used as an umbrella term to cover topics that have been studied in various fields in isolation. By examining all of these existing studies in light of the unified framework of granular computing and extracting their commonalities, it may be possible to develop a general theory for problem solving. In a more philosophical sense, granular computing can describe a way of thinking that relies on the human ability to perceive the real world under various levels of granularity (i.e., abstraction) in order to abstract and consider only those things that serve a specific interest and to switch among different granularities. By focusing on different levels of granularity, one can obtain different levels of knowledge, as well as a greater understanding of the inherent knowledge structure. Granular computing is thus essential in human problem solving and hence has a very significant impact on the design and implementation of intelligent systems. See also Rough Sets, Discretization Type-2 Fuzzy Sets and Systems References . Bargiela, A. and Pedrycz, W. (2003) Granular Computing. An introduction, Kluwer Academic Publishers . . . . . . . . . . . . . . . . . . . . . . . . . Yao, Y.Y. (2004) "A Partition Model of Granular Computing", Lecture Notes in Computer Science (to appear) Zadeh, L.A. (1997) "Toward a Theory of Fuzzy Information Granulation and its Centrality in Human Reasoning and Fuzzy Logic", Fuzzy Sets and Systems, 90:111-127 . Theoretical computer science Machine learning