DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Data Mining Data Mining with Rattle Introduction Data Transform Explore A Model Building Framework Unsupervised Modelling Two Class Models Multi Class Models Regression Models Text Mining Evaluation and Deployment Moving into R Troubleshooting R for the Data Miner R Data Graphics in R Understanding Data Preparing Data Building Models Evaluating Models Algorithms Apriori Bagging Bayes Classifier Boosting Cluster Analysis Conditional Trees Hierarchical Clustering K-Means K-Nearest Neighbours Linear Models Logistic Regression Neural Networks Support Vector Machines Text Mining Open Products AlphaMiner Borgelt Data Mining Suite KNime R Rattle Weka Closed Products C4.5 Clementine Equbits Foresight GhostMiner InductionEngine ODM Enterprise Miner Statistica Data Miner TreeNet Virtual Predict Appendicies Glossary Bibliography Index

Examples

R provides the svm in e1071 as an interface to LIBSVM, which provides a very efficient and fast implementation.

  library(e1071)
  iris.svm <- svm(Species ~ ., data=iris, probability=TRUE)
  plot(iris.svm, iris, Petal.Width ~ Petal.Length,
          slice = list(Sepal.Width = 3, Sepal.Length = 4))
  pred <- predict(iris.svm, iris, probability = TRUE)
  attr(pred, "prob") # to get the probabilities

kernlab for kernel learning provides ksvm and is more integrated into R so that different kernels can easily be explored. A new class called kernel is introduced, an kernel functions are objects of this class.

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