search for: sigest

hello, I have the following problem setting parameter 'frac' in the sigest function of the kernlab package. ## executing the ?sigest example: library(kernlab) data(spam) srange <- sigest(type~.,data = spam) ## works fine... ## setting 'frac' explicitly ## (in this case even to the default of .25) options(error=recover) srange <- sigest(type~.,data = spam,...

...ing() : Kernel-based ranking method o onlearn() : Kernel-based Online Learning algorithms for classification, novelty detection and regression o kpca() : Kernel Pricipal Components Analysis o kcca() : Kernel Canonical Correlation Analysis o kfa() : Kernel Feature Analysis o sigest() : Hyperparameter estimation for the Gaussian and the Laplacian kernels o inchol() : Incomplete Cholesky decomposition method o csi() : Cholesky decomposition with side information o ipop() : Interior point-based Quadratic Optimizer Kernlab also includes a range of functions enabling the ea...

...ing() : Kernel-based ranking method o onlearn() : Kernel-based Online Learning algorithms for classification, novelty detection and regression o kpca() : Kernel Pricipal Components Analysis o kcca() : Kernel Canonical Correlation Analysis o kfa() : Kernel Feature Analysis o sigest() : Hyperparameter estimation for the Gaussian and the Laplacian kernels o inchol() : Incomplete Cholesky decomposition method o csi() : Cholesky decomposition with side information o ipop() : Interior point-based Quadratic Optimizer Kernlab also includes a range of functions enabling the ea...

...tor(Class), class.weights= c("0" =99, "1" =1), prob.model=T) > or > ksvm_model1<-ksvm(as.matrix(slides), as.factor(Class), class.weights=wts, prob.model=T) > with the named vector wts 0 1 99 1 I get the following output: > Using automatic sigma estimation (sigest) for RBF or laplace kernel Error in inherits(x, "factor") : only 0's may be mixed with negative subscripts In addition: Warning message: Variable(s) `' constant. Cannot scale data. in: .local(x, ...) > My data is a balanced set of 2500 examples, most of the 65 features are b...

...gt; x <- sample(seq(-20,20,0.1), 100) > y <- sin(x)/x + rnorm(100,sd=0.05) > x <- matrix(x, nrow=25, ncol=4) > y <- matrix(y, nrow=25, ncol=4) # build the model: seems successful > foo <- rvm(x, y) # same with ksvm(), gausspr(), ecc. Using automatic sigma estimation (sigest) for RBF or laplace kernel > foo Relevance Vector Machine object of class "rvm" Problem type: regression Gaussian Radial Basis kernel function. Hyperparameter : sigma = 0.00179432103430767 Number of Relevance Vectors : 7 Variance : 0.05937295 Training error : 0.049660537 # but pr...

Hi, My question might be a little general. I have a number of values to select for the complexity parameters in some classifier, e.g. the C and gamma in SVM with RBF kernel. The selection is based on which values give the smallest cross validation error. I wonder if the randomized splitting of the available dataset into folds is done only once for all those choices for the parameter values, or

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"

...ar)&&(class(kernel)=="rbfkernel" || class(kernel) =="laplacedot" || kernel == "laplacedot"|| kernel=="rbfdot")){ 154 kp <- match.arg(kpar,"automatic") 155 if(kp=="automatic") 156 kpar <- list(sigma=mean(sigest(x,scaled=FALSE)[c(1,3)])) 157 cat("Using automatic sigma estimation (sigest) for RBF or laplace kernel","\n") 158 159 } 160 if(!is(kernel,"kernel")) 161 { 162 if(is(kernel,"function")) kernel <- deparse(substitute(ker...