learnBinaryRule(VHDClassification)
learnBinaryRule()所属R语言包：VHDClassification
Function to learn a binary classification rule
功能学习的二元分类规则
译者：生物统计家园网 机器人LoveR
描述----------Description----------
Function to learn a binary classification rule. For more than two class, use learnPartitionWithLLR instead. The learned rule can be linear or quadratic. There are reduction dimension methods (accessible via argument procedure) to make the procedure efficient when the number of features is larger than the number of observations
功能学习的二元分类规则。对于两个以上的类，使用learnPartitionWithLLR“。据悉规则可以是线性或二次。有降维方法（通过参数程序访问）功能的数量是大于观测值的数量时，使该过程的效率
用法----------Usage----------
learnBinaryRule(x, y,type='linear', procedure = "FDRThresh", covariance = "diagonal", ql = NULL, qq = NULL,prior=FALSE)
参数----------Arguments----------
参数：x
The Matrix with input data of size pxn (p feature space dimension, and n number of observations)
的矩阵大小PXN（对特征空间的维数，并且n的观测数与输入数据）
参数：y
A vector of n factors with 2 LEVELS (labels) associated to observations (can also be numeric)
一个向量n个因素与2级（标签），观察（也可以是数值）
参数：type
'quadratic' or 'linear' are valid types.
“二次”或“线性”是有效的类型。
参数：procedure
Procedure gives the used procedure to reduce the dimensionality of the estimated NormalVector and FormVector. use 'noThresh' for no dimensionality reduction. UnivTresh is the universal threshold and FDRThresh is an FDR thresolding procedure. When type=='linear' 'FANThresh' and 'FDRstudent' are also available. For type linear, the thresholding procedures are fully described in the Paper "Fast rate of convergence in high dimensional linear discriminant analysis"
程序提供了使用的程序，以减少在的估计NormalVector和FormVector的维数。使用“noThresh没有降维。 UnivTresh是通用阈值和FDRThresh是FDR thresolding的过程。当type ==线性FANThresh“和”FDRstudent的，也可提供。型线性阈值的程序充分说明，在高维的线性判别分析“一文的收敛速度快”
参数：covariance
Unused argument ... further development comming soon
未使用的引...进一步发展即将推出
参数：ql
The parameter associated to the thresholding procedure for the estimation of NormalVector. If a vector of values is given a 10 fold cross validation is performed
相关联的参数的阈值化过程为估计NormalVector。如果给出一个向量的值进行10倍交叉验证
参数：qq
The parameter associated to the thresholding procedure for the estimation of FormVector (only when type='quadratic'). If a vector of values is given a 10 fold cross validation is performed
相关联的参数的阈值化过程的估计FormVector（只有当二次类型=）。如果给出一个向量的值进行10倍交叉验证
参数：prior
Do we put a prior on y (taking into account the proportion of the different class in the learning set to build the classification rule
我们把之前对y（考虑到不同类别的比例在学习组建立的分类规则
值----------Value----------
A classification rule of class LinearRule if type='linear' and of class QuadraticRule if type='quadratic'.
分类规则类LinearRule，如果类型=“线性”和类QuadraticRule，如果=二次。
（作者）----------Author(s)----------
Robin Girard
参考文献----------References----------
Fast rate of convergence in high dimensional linear discriminant analysis. R. Girard To appear in Journal of Nonparametric Statistics.\ Very high dimensional discriminant analysis with thresholding estimation. R. Girard. Submitted.
参见----------See Also----------
learnPartitionWithLLR
learnPartitionWithLLR
实例----------Examples----------
p=100; n=50 ; mu=array(0,c(p,2)); mu[1:10,1]=1 ;C=array(c(1,20),p)
x=NULL; y=NULL;
for (k in 1:2){ x=rbind(x,t(array(C^(1/2),c(p,n))*(matrix(rnorm(p*n),nrow=p,ncol=n))+array(mu[,k],c(p,n))));
y=c(y,array(k,n)) }
#Learning[学习]
LearnedBinaryRule=learnBinaryRule(x,y)
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 16:02:40
