Information Geometric Model Selection Criterion and its Application in Cognition

Author

Liu, Yun-Hui ; Luo, Si-Wei ; Lv, Zi-Ang ; Huang, Hua

Author_Institution

Dept. of Comput. Sci., Beijing Jiaotong Univ.

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

2814

Lastpage

2817

Abstract

Model selection is important in deciding among competing computational models in many scientific research domains including in cognition processing. This paper presents an information geometric model selection criterion GMSC and shows its application in cognition. IGMSC computes the geometric complexity of the model by regarding the model space as the manifold and estimates the model-data geometric fitness by using the divergence between the true distribution and the asymptotic distribution, enduing complexity and fitness with clear geometric significance. The comparison experiment shows the effect of IGMSC in cognition

Keywords

cognition; computational complexity; computational geometry; asymptotic distribution; cognition processing; computational model; geometric complexity; information geometric model selection criterion; model-data geometric fitness; Application software; Cognition; Cognitive science; Computational modeling; Computer science; Cybernetics; Distributed computing; Electronic mail; Information geometry; Machine learning; Probability distribution; Solid modeling; IGMSC; Model selection; cognition; information geometry;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.259004

Filename

4028540