similar to: ksvm-kernel

Hello, I am using kernlab to do some binary classification on aminoacid strings. I am using a custom kernel, so i use the kernel="matrix" option of the ksvm method. My (normalized) kernel matrix is of size 1309*1309, my results vector has the same length. I am using C-svc. My kernlab call is something similiar to this: ksvm(kernel="matrix", kernelMatrix, trainingDataYs,

Hi, A. In a nutshell: The training error, obtained as "error (ret)", from the return value of a ksvm () call for a eps-svr model is (likely) being computed wrongly. "nu-svr" and "eps-bsvr" suffer from this as well. I am attaching three files: (1) ksvm.R from the the kernlab package, un-edited, (2) ksvm_eps-svr.txt: (for easier reading) containing only eps-svr

Hello, I have noticed a bug with LSSVM implementation in R. It could be a bug with the LSSVM itself that causes this problem. I thought I should post this message to see if anyone else is familiar with this problem and explain why the result is different for odd and even number of cases. Once the hyperplane is found using LSSVM, the prediction results vary when you predict odd or even number of

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 <-

hi, I am using R's "kernlab" package, exactly i am doing classification using ksvm(.) and predict.ksvm(.).I want use of custom kernel. I am getting some error. # Following R code works (with promotergene dataset): library("kernlab") s <- function(x, y) { sum((x*y)^1.25) } class(s) <- "kernel" data("promotergene") gene <- ksvm(Class ~ .,

I use the svm package kernlab .I have two question. In R library(kernlab) m=ksvm(xtrain,ytrain,type="C-svc",kernel=custom function, C=10) alpha(m) alphaindex(m) I can get alpha value and alpha index about package. 1. Assumption that number of sample are 20. number of support vectors are 15. then rest 5`s alphas are 0? 2. I want use kernelMatrix xtrain=as.matrix(xtrain)

Hi useR's, I am resending this request since I got no response for my last post and I am new to the list so pardon me if I am violating the protocol. I am trying to use the "Kernlab" package for training and prediction using SVM's. I am getting the following error when I am trying to use the predict function: > predictSvm = predict(modelforSVM, testSeq); Error in

Hi, this is a question about the R package kernlab. I use kernlab as a library in a C++ program. The host application defines a graph kernel (defined by me), generates a gram matrix and trains kernlab directly on this gram matrix, like this: regm<-ksvm(K,y,kernel="matrix"), where K is the n x n gram kernelMatrix of my kernel, and y is the R-vector of quantitative target values.

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"

Hello list, I am faced with a two-class classification problem with highly asymetric class sizes (class one: 99%, class two: 1%). I'd like to obtain a class probability model, also introducing available information on the class prior. Calling kernlab/ksvm with the line > ksvm_model1<-ksvm(as.matrix(slides), as.factor(Class), class.weights= c("0" =99, "1" =1),

Hello all, I would like to use the train function from the caret package to train a svm with a spectral kernel from the kernlab package. Sadly a svm with spectral kernel is not among the many methods in caret... using caret to train svmRadial: ------------------ library(caret) library(kernlab) data(iris) TrainData<- iris[,1:4] TrainClasses<- iris[,5] set.seed(2)

Hello, I would like to know, whether for the support vector classification function ksvm() the response values stored in object at ymatrix are cross validated outputs/predictions: Example code from package kernlab, function ksvm: library(kernlab) ## train a support vector machine filter <- ksvm(type~.,data=spam,kernel="rbfdot",kpar=list(sigma=0.05),C=5,cross=3) filter filter at

I got the data working, but now I got another problem with KSVM: line search fails -2.793708 -0.5831701 1.870406e-05 -5.728611e-06 -5.059796e-08 -3.761822e-08 -7.308871e-13Error in prob.model(object)[[p]]$A : $ operator is invalid for atomic vectors On Tue, Jul 7, 2009 at 6:45 PM, Steve Lianoglou<mailinglist.honeypot at gmail.com> wrote: > Hi, > > On Jul 7, 2009, at 6:44 PM,

Dear list, I am using the ksvm function from kernlab as follows: (1) learning > svm.pol4 <- ksvm(class.labs ~ ., data = train.data, prob.model = T, scale = T, kernel = "polydot") (2) prediction > svm.pol.prd4 <- predict(svm.pol4, train.data, type = "probabilities")[,2] But unfortunately, when calling the prediction, once in every 10s of times (using the exact

What's wrong? Very sad about this... model <- ksvm(x=mytraindata[, -1], y=factor(mytraindata[, 1]), prob.model=T) Error in .local(x, ...) : x and y don't match.

Hello all, I'm trying to run a gird parameter search for a svm. Therefore I'M using the ksvm function from the kernlab package. ---- svp <- ksvm(Ktrain,ytrain,type="nu-svc",nu=C) ---- The problem is that the optimization algorithm does not return for certain parameters. I tried to use setTimeLimit() but that doesn't seem to help. I suspect that ksvm() calls c code that

I am using kernlab to build svm models. I am not sure how to access the different slots of the object. For instance if I want to get the nuber of support vectors for each of model I am building and store it in a vector. >ksvm.model <- ksvm(Class ~ ., data = somedata,kernel = "vanilladot", cross = 10, type ="C-svc") >names(attributes(ksvm.model)) [1] "param"

Hi, I'm working on a project using the kernlab library. For one phase, I want the "decision values" from the SVM prediction, not the class label. the e1071 library has this function, but I can't find the equivalent in ksvm. In general, when an SVM is used for classification, the label of an unknown test-case is decided by the "sign" of its resulting value as

Hi there, I'm trying to do a regression experiment on a multidimensional dataset where both x and y in the model are multidimensional vectors. I'm using R version 2.9.2, updated packages, on a Linux box. I've tried gausspr(), ksvm() and rvm(), and the models are computed fine, but I'm always getting the same error message when I try to use predict(): "Error in

Hi, I have a process using svm from the e1071 library. it works. I want to try using the KSVM library instead. The same data used wiht e1071 gives me an error with KSVM. My data is a data.frame. sample code: svm_formula <- formula(y ~ a + B + C) svm_model <- ksvm(formula, data=train_data, type="C-svc", kernel="rbfdot", C=1) I get the following error: