# Support Vector Regression - PowerPoint PPT Presentation

1 / 16

Support Vector Regression. Artur Akbarov. Paper. A tutorial on support vector regression By Smola, A.J and Schölkopf , B. Statistics and Computing, 14, pp . 199-222 , 200 4. Why SVM?.

I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.

Support Vector Regression

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.

- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

## Support Vector Regression

ArturAkbarov

### Paper

A tutorial on support vector regression

By Smola, A.J and Schölkopf, B.

Statistics and Computing, 14,

pp. 199-222, 2004

### Why SVM?

• SVM is applied in many different fields including bioinformatics, epidemiology, finance, economics etc.

• Essentially, it can be applied wherever there is a problem of classification or prediction.

Linear SVR:

Non-linear SVR:

• Polynomial

### The solution of SVR

• Model fitting:

• Training set – fit the model

• Validation set – predict using the fitted model,

choose the model with minimum

prediction error.

• Model testing:

• Test set – examine the prediction error (model performance, compare different prediction methods)

### Splitting the data set

• Training and validation sets:

• Fixed split

• Random split

• Cross-validation

• Split the data into n number of subsets, train on n-1 subsets, validate on the remaining subset, loop over all subsets.

• Leave-one-out cross validation.

### SVM library

• LIBSVM – SVM library in different languages.

• Weka – data mining tools.

• R package - “e1071”

### SVM in R

• install.packages(“e1071”)

• library(“e1071”)

• model<-svm(data=D, formula=Y~X1+X2)

• model<-svm(y=Y,x=X)

• Y_fit<-predict(model, X)

• Y_hat<-predict(model, X_new)

### SVM in R

• Other SVM parameters for the svm() function:

• epsilon = 0.1

• cost = 1.0, which is C

• kernel =“linear”, “polynomial”, “radial”, ”sigmoid”.

### Tuning SVM parameters

• best.svm() function uses grid search to find the optimal values for SVM parameters.

• model<-best.svm(x=X, y=Y, tunecontrol=tune.control(cross=5), cost=c(1:10),epsilon=c(0.05,0.10))