INFORMATION GEOMETRIC MODEL SELECTION CRITERION AND ITS APPLICATION IN COGNITION

摘要：

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-IGMSC 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.

关键词： Model selection information geometry IGMSC cognition

作者: YUN-HUI LIU SI-WEI LUO ZI-ANG LV HUA HUANG

作者单位: Department of Computer Science, Beijing Jiaotong University, Beijing, 100044, China

会议类型: 国际会议

会议名称: 2006 International Conference on Machine Learning and Cybernetics(IEEE第五届机器学习与控制论坛)

会议地点: 大连

会议语种:英文

页码: 2814-2817

在线出版日期: 2006-08-13（万方平台首次上网日期，不代表论文的发表时间）