Displaying 20 results from an estimated 1000 matches similar to: "SVM performance using laplace kernel is too slow"
2008 Jun 25
1
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 <-
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
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:
>
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
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
2012 Jul 31
1
kernlab kpca predict
Hi!
The kernlab function kpca() mentions that new observations can be transformed by using predict. Theres also an example in the documentation, but as you can see i am getting an error there (As i do with my own data). I'm not sure whats wrong at the moment. I haven't any predict functions written by myself in the workspace either. I've tested it with using the matrix version and the
2011 Oct 06
0
linear classifiers with sparse matrices
I've been trying to get some linear classifiers (LiblineaR, kernlab,
e1071) to work with a sparse matrix of feature data. In the case of
LiblineaR and kernlab, it seems I have to coerce my data into a dense
matrix in order to train a model. I've done a number of searches,
read through the manuals and vignettes, but I can't seem to see how to
use either of these packages with sparse
2012 Dec 10
0
Time Series Prediction using Gaussian Process
*Hello All,*
I tried figuring out the problem, I was trying to use laplacedot to predict
the long term, which however would not do a good job.
Then, I tried to do a point by point prediction and building the model
again, everytime. It shows me better results. I tried writing my kernel
function (matern covariance function), and attached is the result of that.
Red lines show the fit and blue lines
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
2009 Oct 23
1
Data format for KSVM
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:
2008 Sep 14
0
ksvm accessing the slots of S4 object
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"
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)
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
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()
2010 Aug 29
0
SVM comparison
I'm trying to run an epsilon regression model, and am comparing the results
between e1071 and kernlab. I believe that I'm calling the ksvm and svm
functions the same way but I'm getting different results:
library(e1071); library(kernlab)
ksvm(x=1:100, y=(1:100)/5, type="eps-svr", kpar=list(sigma=1))
svm(x=1:100, y=(1:100)/5, type="eps-regression", gamma=0.5)
I
2008 Jul 21
0
SVM: Graphical representation
Hi,
We are working on binary classification using kernlab for SVM based on more than 30 variables and now we want to provide a graphical representation of our results in 2D or 3D.
We have checked the graphical functionality of kernlab but it seems that only works with 2 principal components, and we use to work with more than 8 PC due to the variability of our data. We are thinking in some kind
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),
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