摘要
针对基于L1范数的Lasso回归与基于L2范数的Ridge回归模型,分别讨论两种分类器的设计方法,即基于Lasso回归的全体与类样本分类器和基于Ridge回归的全体与类样本分类器。分别在2个大样本数据库与2个小样本数据库对所给出的方法进行比较研究与分析,结果表明基于全体样本的分类器更适合小样本问题,而基于类样本的分类器更适合大样本问题。
By examining Ll-paradigm based Lasso regression and L2-paradigm based Ridge regression models, the design methods of two classifiers, i. e. , Lasso regression based population and class sample classifier, and Ridge regression based population and class sample classifier, are separately discussed. Comparative study and analysis are carried out separately upon 2 large sample size databases and 2 small sample size databases. Results show that population sample based classifier suits better for small sample size problems whereas class sample based classifier suits better for large sample size problems.
出处
《计算机应用与软件》
CSCD
2011年第11期287-289,共3页
Computer Applications and Software
关键词
回归分析
分类器
小样本问题
大样本问题
Regression analysis Classifier Small sample size problem Large sample size problems