1. R: SVM
sesejun@is.ocha.ac.jp
2010/12/08

2. SVM
> iris.train <- read.table("iris_train.csv", sep=",", header=T)
> iris.test <- read.table("iris_test.csv", sep=",", header=T)
> library("e1071")
> iris.model <- svm(iris.train[1:4], iris.train$Class)
> iris.pred <- predict(iris.model, iris.test[1:4])
> table(iris.pred, iris.test$Class)
iris.pred Iris-setosa Iris-versicolor Iris-virginica
Iris-setosa 7 0 0
Iris-versicolor 0 9 0
Iris-virginica 0 0 14
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3. > iris.model <- svm(iris.train[1:4], iris.train$Class, kernel=”linear”)
> iris.pred <- predict(iris.model, iris.test[1:4])
> table(iris.pred, iris.test$Class)
iris.pred Iris-setosa Iris-versicolor Iris-virginica
Iris-setosa 7 0 0
Iris-versicolor 0 9 0
Iris-virginica 0 0 14
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4. 4
1. USPS
1. USPS SVM
radial
2. K-NN
3. SVM K-NN
• 1 6 15:00 ( )
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