基于函数集信息量的模型选择研究

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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.

Key words： Subspace Information Quantity（SIQ） Function Set Information Quantity（FSIQ） Model selection Statistical learning theory

Received: 09 December 2003

PACS:

TP18

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	Sheng Shou-zhao
	Wang Dao-bo
	Wang Zhi-sheng
	Huang Xiang-hua

Cite this article:

Sheng Shou-zhao,Wang Dao-bo,Wang Zhi-sheng等. Research on Model Selection Based on Function Set Information Quantity[J]. , 2005, 27(4): 552-555 .

URL:

http://jeit.ie.ac.cn/EN/ OR http://jeit.ie.ac.cn/EN/Y2005/V27/I4/552

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