Datamining r 4_5th
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Datamining r 4_5th

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Datamining r 4_5th Datamining r 4_5th Presentation Transcript

  • R: SVMsesejun@is.ocha.ac.jp 2010/12/08
  • 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 2
  • > 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 3
  • 41. USPS 1. USPS SVM radial 2. K-NN 3. SVM K-NN• 1 6 15:00 ( ) 4