scholarly journals On a Quantitative Measure for Modularity Based on Information Theory

2005 ◽  
pp. 393-402 ◽  
Author(s):  
Daniel Polani ◽  
Peter Dauscher ◽  
Thomas Uthmann
Keyword(s):  
Information Theory ◽  
Quantitative Measure

Recent results in information theory

1966 ◽  
Vol 3 (1) ◽  
pp. 1-93 ◽  
Author(s):  
Samuel Kotz
Keyword(s):  
Information Theory ◽  
Communication Channel ◽  
Quantitative Measure ◽  
Strict Sense ◽  
Bell System Technical Journal ◽  
Noisy Channels ◽  
Basic Theorem ◽  
Reliable Transmission ◽  
Transmission Of Information ◽  
Communications System

Information theory, in the strict sense, is a rapidly developing branch of probability theory originating from a paper by Claude E. Shannon in the Bell System Technical Journal in 1948,in which anew mathematical model ofcommunications systems was proposed and investigated.One of the central innovations of this model was in regarding the prime components of a communications system (the source of messages and the communication channel) as probabilistic entities. Shannon also proposed a quantitative measure of the amount of information based on his notion of entropy and proved the basic theorem of this theory concerning the possi bility of reliable transmission of information over a particular class of noisy channels.

Recent results in information theory

1966 ◽  
Vol 3 (01) ◽  
pp. 1-93 ◽  
Author(s):  
Samuel Kotz
Keyword(s):  
Information Theory ◽  
Communication Channel ◽  
Quantitative Measure ◽  
Strict Sense ◽  
Bell System Technical Journal ◽  
Noisy Channels ◽  
Basic Theorem ◽  
Reliable Transmission ◽  
Transmission Of Information ◽  
Communications System

Information theory, in the strict sense, is a rapidly developing branch of probability theory originating from a paper by Claude E. Shannon in theBell System Technical Journalin 1948,in which anew mathematical model ofcommunications systems was proposed and investigated.One of the central innovations of this model was in regarding the prime components of a communications system (the source of messages and the communication channel) as probabilistic entities. Shannon also proposed a quantitative measure of the amount of information based on his notion of entropy and proved the basic theorem of this theory concerning the possi bility of reliable transmission of information over a particular class of noisy channels.

Constructing and Deconstructing Concepts

2016 ◽  
Vol 63 (5) ◽  
pp. 249-262 ◽  
Author(s):  
Charles A. Doan ◽  
Ronaldo Vigo
Keyword(s):  
Information Theory ◽  
Model Performance ◽  
Unsupervised Classification ◽  
Family Resemblance ◽  
Category Membership ◽  
Formal Models ◽  
Human Beings ◽  
One Dimensional ◽  
Relational Patterns ◽  
Optimal Classification

Abstract. Several empirical investigations have explored whether observers prefer to sort sets of multidimensional stimuli into groups by employing one-dimensional or family-resemblance strategies. Although one-dimensional sorting strategies have been the prevalent finding for these unsupervised classification paradigms, several researchers have provided evidence that the choice of strategy may depend on the particular demands of the task. To account for this disparity, we propose that observers extract relational patterns from stimulus sets that facilitate the development of optimal classification strategies for relegating category membership. We conducted a novel constrained categorization experiment to empirically test this hypothesis by instructing participants to either add or remove objects from presented categorical stimuli. We employed generalized representational information theory (GRIT; Vigo, 2011b , 2013a , 2014 ) and its associated formal models to predict and explain how human beings chose to modify these categorical stimuli. Additionally, we compared model performance to predictions made by a leading prototypicality measure in the literature.

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