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Dear R-programmer, I wrote an adapted implementation of the Kennard-Stone algorithm for sample selection of multivariate data (R 2.7.1 under MacBook Pro, Processor 2.2 GHz Intel Core 2 Duo, Memory 2 GB 667 MHZ DDR2 SDRAM). I used for the heart of the script three embedded loops. This makes it especially for huge datasets very slow. For a datamatrix of 1853*1853 and the selection of 556

Dear R-community, I was using SVM regression (svm {e1071}) for predictions of single soil properties of a huge data set (3000 samples). There are for the eps-regression using the radial basis kernel three optimization parameters needed. To make things easier (using only two optimization parameters and not loosing performance) I wanted to use LS SVM regression (lssvm{kernlab}). But it