DATA MINING Desktop Survival Guide by Graham Williams
Desktop Survival Project Home List of Figures List of Tables Data Mining Introduction Data Mining with Rattle Data Exploring Data Transforming Data Cluster Analysis Association Analysis Classification and Regression Models Evaluation and Deployment Issues Moving into R Troubleshooting Beyond Rattle R Data Graphics in R Understanding Data Preparing Data Descriptive and Predictive Analytics Issues Evaluating Models Reporting Cluster Analysis Text Mining Text Mining Algorithms Bagging Bayes Classifier Cluster Analysis Conditional Trees Hierarchical Clustering K-Nearest Neighbours Linear Models Neural Networks Support Vector Machines 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 Installing GTK, R, and Rattle Glossary Bibliography Index

Examples

R provides the XnullXR functionsR functions (R function)R functionsR libraries (R library)R functionsR option (R option)R functionsR packages (R package)R functionsDatasets (Dataset)R functionsR functionssvm 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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