%0 Journal Article
%T Research on Model Selection Based on Function Set Information Quantity<br>基于函数集信息量的模型选择研究
%A Sheng Shou-zhao
%A Wang Dao-bo
%A Wang Zhi-sheng
%A Huang Xiang-hua
%A <br>盛守照
%A 王道波
%A 王志胜
%A 黄向华
%J 电子与信息学报
%D 2005
%I 
%X 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.
%K Subspace Information Quantity(SIQ)
%K Function Set Information Quantity(FSIQ)
%K Model selection
%K Statistical learning theory<br>子空间信息量
%K 函数集信息量
%K 模型选择
%K 统计学习理论
%U http://www.alljournals.cn/get_abstract_url.aspx?pcid=5B3AB970F71A803DEACDC0559115BFCF0A068CD97DD29835&cid=1319827C0C74AAE8D654BEA21B7F54D3&jid=EFC0377B03BD8D0EF4BBB548AC5F739A&aid=8388C79F256E39A0&yid=2DD7160C83D0ACED&vid=DB817633AA4F79B9&iid=E158A972A605785F&sid=385E3C2062167B88&eid=AF4A4411BB448A36&journal_id=1009-5896&journal_name=电子与信息学报&referenced_num=1&reference_num=16