Abstract:The concepts of the Subspace Information Quantity(SIQ) and Function Set Information Quantity(FSIQ) are presented; Then the problem of model selection based on FSIQ are discussed explicitly, and the approximate method of model selection based on limited samples with white noise is proposed, which resolves the problem of underletting and overfitting of model selection and improves the generalization of predict model well. A new suboptimal algorithm for model selection is given, and its reliability and advantage are illustrated through concrete test.
盛守照; 王道波; 王志胜; 黄向华. 基于函数集信息量的模型选择研究[J]. 电子与信息学报, 2005, 27(4): 552-555 .
Sheng Shou-zhao; Wang Dao-bo; Wang Zhi-sheng; Huang Xiang-hua. Research on Model Selection Based on Function Set Information Quantity. , 2005, 27(4): 552-555 .