如何選擇要使用的 SVM 內核？

如果沒有提前測試，我無法確定我應該在非線性 SVM 中使用什麼內核。我想知道是否有其他方法可以在沒有測試的情況下確定最佳內核？它與數據有什麼關係？

使用幾個不同的內核進行分析。確保你交叉驗證。選擇在交叉驗證期間表現最佳的內核並將其擬合到您的整個數據集。

/edit：這是 R 中的一些示例代碼，用於分類 SVM：

#Use a support vector machine to predict iris species
library(caret)
library(caTools)

#Choose x and y
x <- iris[,c("Sepal.Length","Sepal.Width","Petal.Length","Petal.Width")]
y <- iris$Species

#Pre-Compute CV folds so we can use the same ones for all models
CV_Folds <- createMultiFolds(y, k = 10, times = 5)

#Fit a Linear SVM
L_model <- train(x,y,method="svmLinear",tuneLength=5,
   trControl=trainControl(method='repeatedCV',index=CV_Folds))

#Fit a Poly SVM
P_model <- train(x,y,method="svmPoly",tuneLength=5,
   trControl=trainControl(method='repeatedCV',index=CV_Folds))

#Fit a Radial SVM
R_model <- train(x,y,method="svmRadial",tuneLength=5,
   trControl=trainControl(method='repeatedCV',index=CV_Folds))

#Compare 3 models:
resamps <- resamples(list(Linear = L_model, Poly = P_model, Radial = R_model))
summary(resamps)
bwplot(resamps, metric = "Accuracy")
densityplot(resamps, metric = "Accuracy")

#Test a model's predictive accuracy Using Area under the ROC curve
#Ideally, this should be done with a SEPERATE test set
pSpecies <- predict(L_model,x,type='prob')
colAUC(pSpecies,y,plot=TRUE)

引用自：https://stats.stackexchange.com/questions/10551