摘要
提出了子空间信息量(SIQ)和函数集信息量(FSIQ)概念,详细讨论了基于函数集信息量的模型选择问 题,给出了有限含噪声样本下模型选择的近似解决方法,很好地克服了模型选择过程中普遍存在的欠学习和过学列 问题,大大提高了预测模型的泛化性能,在此基础上提出了一种可行的次优模型选择算法。最后通过具体实例验证 了上述方法的可行性和优越性。
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 underfilling and overfitting of mode! 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.
出处
《电子与信息学报》
EI
CSCD
北大核心
2005年第4期552-555,共4页
Journal of Electronics & Information Technology
基金
航空基金(01C52015)资助课题
关键词
子空间信息量
函数集信息量
模型选择
统计学习理论
Subspace Information Quantity(SIQ), Function Set Information Quantity(FSIQ), Model selection, Statistical learning theory
