search for: stringdot

Hi!! I am trying to figure out how to use the string kernel "stringdot" in kernlab. k <- function(x,y) { (sum(x*y) +1)*exp(-0.001*sum((x-y)^2)) } class(k) <- "kernel" data(promotergene) ## train svm using custom kernel gene.k <- ksvm(Class~.,data=promotergene,kernel=k,C=10,cross=5) # works fine in this case gene.rbf <- ksvm(Class~.,d...

Dear all, I am using kernlab package in R, and I have amino acid sequences with different lenghts as input for a SVM and I need to go through this sequences using windows (sliding or fixed) of size X. Does anyone has any suggestions about which function I should use? I thought I could use stringdot, but I am not sure whether it will do what I need.., I have defined my stringdot as: mystringdot <- stringdot(length = 7, lambda = 0.5, type = "sequence", normalized = TRUE) and my svm as: ksvm(mydata[,1],data=mydata,type="C-svc",kernel=mystringdot,C=10) but it doesn'...

kernlab version 0.9-7 is now online and incorporates : + a much improved fast implementation of string kernels "stringdot" based on suffix arrays. + a new kernel method kmmd() which implements a non-parametric kernel based two sample test. The new kernlab version also includes many minor improvements and fixes. Alexandros _______________________________________________ R-packages mailing list R-pac...

kernlab version 0.9-7 is now online and incorporates : + a much improved fast implementation of string kernels "stringdot" based on suffix arrays. + a new kernel method kmmd() which implements a non-parametric kernel based two sample test. The new kernlab version also includes many minor improvements and fixes. Alexandros _______________________________________________ R-packages mailing list R-pac...

...", preProcess = c("center", "scale"), metric = "cRand", tuneLength = 4) svmNew ------------------- here is an example on how to train the ksvm with spectral kernel ------------------- # Load the data data(reuters) y <- rlabels x <- reuters sk <- stringdot(type="spectrum", length=4, normalized=TRUE) svp <- ksvm(x,y,kernel=sk,scale=c(),cross=5) svp ----------------- Does anyone know how I can train the svm from above with using the caret package? best regards [[alternative HTML version deleted]]

Hi everybody, I''m using R on a 64-bit Ubuntu 9.04 (Jaunty). I prefer to install R packages from source, even if they are available in Synaptic. The problem is that I can''t install/compile kernlab. Everything works fine until it gets to the lazy loading part: ** preparing package for lazy loading Creating a new generic function for "terms" in "kernlab"

...1727 .classAgreement <- function (tab) { ... 1737 } 1738 1739 ## List Interface 1740 1741 1742 setMethod("ksvm",signature(x="list"), 1743 function (x, 1744 y = NULL, 1745 type = NULL, 1746 kernel = "stringdot", 1747 kpar = list(length = 4, lambda = 0.5), 1748 C = 1, 1749 nu = 0.2, 1750 epsilon = 0.1, 1751 prob.model = FALSE, 1752 class.weights = NULL, 1753 cross = 0, 1754 fit...