similar to: Kernlab: problem with small datasets

Displaying 20 results from an estimated 600 matches similar to: "Kernlab: problem with small datasets"

2009 Apr 28
1
kernlab - custom kernel
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 ~ .,
2011 Aug 26
1
kernlab: ksvm() bug?
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
2012 Aug 19
1
kernlab | ksvm error
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
2007 Oct 30
0
kernlab/ ksvm: class.weights & prob.model in binary classification
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),
2007 Aug 14
0
kernlab ksvm() cross-validation prediction response vector
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
2012 Aug 27
0
kernlab`s custom kernel of ksvm freeze
Hello, together I'm trying to use user defined kernel. I know that kernlab offer user defined kernel(custom kernel functions) in R. I used data spam including package kernlab. (number of variables=58 number of examples =4061) i'm user defined kernel's form, kp=function(d,e){ as=v*d bs=v*e cs=as-bs cs=as.matrix(cs) exp(-(norm(cs,"F")^2)/2) }
2011 May 28
0
how to train ksvm with spectral kernel (kernlab) in caret?
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)
2009 Oct 06
0
Kernlab: multidimensional targets in rvm(), ksvm(), gausspr()
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
2007 Sep 12
0
one-class SVM in kernlab
Hello, I'm trying to using ksvm() in the kernlab package to fit a one-class SVC, but I get a strage result on the cross-validation error estimate. For example, consider this code: data(spam) classifier <- ksvm(type~.,data=spam[which(spam[,'type']=='spam'),], type="one-svc",kernel="rbfdot",kpar=list(sigma=0.1),nu=0.05,cross=10) what I get is: >
2006 Nov 24
1
How to find AUC in SVM (kernlab package)
Dear all, I was wondering if someone can help me. I am learning SVM for classification in my research with kernlab package. I want to know about classification performance using Area Under Curve (AUC). I know ROCR package can do this job but I found all example in ROCR package have include prediction, for example, ROCR.hiv {ROCR}. My problem is how to produce prediction in SVM and to find
2011 May 26
0
R svm prediction kernlab
Hi All, I am using ksvm method in kernlab R package for support vector machines. I learned the multiclass one-against-one svm from training data and using it to classify new datapoints. But I want to update/finetune the 'svm weights' based on some criteria and use the updated svm weights in the predict method framework. I don't know if its possible or not, how do classify new
2009 Dec 25
2
Help with SVM package Kernlab
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
2012 Nov 29
1
Help with this error "kernlab class probability calculations failed; returning NAs"
I have never been able to get class probabilities to work and I am relatively new to using these tools, and I am looking for some insight as to what may be wrong. I am using caret with kernlab/ksvm. I will simplify my problem to a basic data set which produces the same problem. I have read the caret vignettes as well as documentation for ?train. I appreciate any direction you can give. I
2007 Dec 17
0
kernlab and gram matrix
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.
2006 Nov 27
0
kernlab 0.9-0 on CRAN
A new version of kernlab has just been released. kernlab is a kernel-based Machine Learning package for R. kernlab includes the following functions: o ksvm() : Support Vector Machines for classification, regression, novelty detection, native multi-class classification, support for class-probability output and confidence intervals in regression. o gausspr()
2006 Nov 27
0
kernlab 0.9-0 on CRAN
A new version of kernlab has just been released. kernlab is a kernel-based Machine Learning package for R. kernlab includes the following functions: o ksvm() : Support Vector Machines for classification, regression, novelty detection, native multi-class classification, support for class-probability output and confidence intervals in regression. o gausspr()
2012 Sep 13
0
I need help for svm package kernlab in R
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)
2009 Dec 24
0
Error with Package "Kernlab" for SVM prediction
Hi All, 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 `contrasts<-`(`*tmp*`, value = "contr.treatment") : contrasts can be applied only to factors with 2 or more levels The training file is a
2010 Sep 24
0
kernlab:ksvm:eps-svr: bug?
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
2009 Oct 04
3
error installing/compiling kernlab
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"