如何计算R包LiblineaR中的支持向量数

例如，是否有一种方法可以计算此分类模型中支持向量的数量

library(LiblineaR)
data(iris)
attach(iris)

x <- iris[,1:4]
y <- factor(iris[, 5])
set.seed(1)
train <- sample(1:dim(iris)[1],100)
detach(iris)

xTrain <- x[train,]
xTest <- x[-train,]
yTrain <- y[train]
yTest <- y[-train]

s <- scale(xTrain, center=T, scale=T)

m <- LiblineaR(data=s, target=yTrain, type=5, cost=0.1)
m

---

liblineaR

包不在模型输出中存储支持向量，但像

kernlab

这样的包可以：

> library kernlab
> your.svm <- ksvm(s,yTrain,type="C-svc",kernel="polydot", 
      prob.model=TRUE,kpar=list(degree=1, scale=1, offset=0))

或者更改参数以运行不同类型的SVM：

> your.svm <- ksvm(s,yTrain,type="one-svc", 
     kernel="splinedot",prob.model=TRUE)
 Setting default kernel parameters  
> your.svm@nSV
[1] 22

> your.svm@nSV
[1] 23

> your.svm <- ksvm(s,yTrain,type="one-svc", 
     kernel="splinedot",prob.model=TRUE)
 Setting default kernel parameters  
> your.svm@nSV
[1] 22

C-svc - C classification
nu-svc - nu classification
C-bsvc - bound-constraint svm classification
spoc-svc - Crammer, Singer native multi-class
kbb-svc - Weston, Watkins native multi-class
one-svc - novelty detection
eps-svr - epsilon regression
nu-svr - nu regression
eps-bsvr - bound-constraint svm regression