支持向量机通过寻找_r语言 支持向量机分类_支持向量机优缺点(5)

sigmoid.svm.fit <- svm(label ~ x y, data = dataSim, kernel ='sigmoid')

with(dataSim, mean(label == ifelse(predict(sigmoid.svm.fit) > 0,1, -1)))

df <- cbind(dataSim,

data.frame(LinearSVM = ifelse(predict(linear.svm.fit) > 0, 1, -1),

PolynomialSVM = ifelse(predict(polynomial.svm.fit) > 0, 1, -1),

RadialSVM = ifelse(predict(radial.svm.fit) > 0, 1, -1),

SigmoidSVM = ifelse(predict(sigmoid.svm.fit) > 0, 1, -1)))

library("reshape")

predictions <- melt(df, id.vars = c('x', 'y'))

library('ggplot2')

ggplot(predictions, aes(x = x, y = y, color = factor(value)))

geom_point()

facet_grid(variable ~ .)

最后，我们回到最开始的那个手写数字的案例，我们试着利用支持向量机重做这个案例。(这个案例的描述与数据参见《R语言与机器学习学习笔记(分类算法)(1)》)

运行代码：

setwd("D:/R/data/digits/trainingDigits")

names<-list.files("D:/R/data/digits/trainingDigits")

data<-paste("train",1:1934,sep="")

for(i in 1:length(names))

assign(data[i],as.vector(as.matrix(read.fwf(names[i],widths=rep(1,32)))))

label<-rep(0:9,c(189,198,195,199,186,187,195,201,180,204))

data1<-get(data[1])

for(i in 2:length(names))

data1<-rbind(data1,get(data[i]))

m <- svm(data1,label,cross=10,type="C-classification")

m

summary(m)

pred<-fitted(m)

table(pred,label)

setwd("D:/R/data/digits/testDigits")

names<-list.files("D:/R/data/digits/testDigits")

data<-paste("train",1:1934,sep="")

for(i in 1:length(names))

assign(data[i],as.vector(as.matrix(read.fwf(names[i],widths=rep(1,32)))))

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