scholarly journals Region Merging Techniques Using Information Theory Statistical Measures

2010 ◽  
Vol 19 (6) ◽  
pp. 1567-1586 ◽  
Author(s):  
Felipe Calderero ◽  
Ferran Marques
Keyword(s):  
Information Theory ◽  
Region Merging ◽  
Statistical Measures ◽  
Merging Techniques

STATISTICAL MEASURES AS MEASURES OF DIVERSITY

2010 ◽  
Vol 03 (02) ◽  
pp. 173-185 ◽  
Author(s):  
OM PARKASH ◽  
A. K. THUKRAL
Keyword(s):  
Information Theory ◽  
Data Analysis ◽  
Information Content ◽  
Biological Data ◽  
Central Tendency ◽  
Information Measures ◽  
Interdisciplinary Field ◽  
Statistical Measures ◽  
Data Statistics

Two fields of research have found tremendous applicability in the analysis of biological data-statistics and information theory. Statistics is extensively used for the measurement of central tendency, dispersion, comparison and covariation. Measures of information are used to study diversity and equitability. These two fields have been used independent of each other for data analysis. In this communication, we develop the link between the two and prove that statistical measures can be used as information measures. Our study will be a new interdisciplinary field of research and it will be possible to describe information content of a system from its statistics.

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.

Sign in / Sign up

Export Citation Format

Share Document