Displaying 20 results from an estimated 300 matches similar to: "ksvm accessing the slots of S4 object"
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 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
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
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
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 <-
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
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:
2012 May 15
1
Regression Analysis or Anova?
Dear all,
I hope to be the clearest I can.
Let's say I have a dataset with 10 variables, where 4 of them represent for
me a certain phenomenon that I call Y.
The other 6 represent for me another phenomenon that I call X.
Each one of those variables (10) contains 37 units. Those units are just
the respondents of my analysis (a survey).
Since all the questions are based on a Likert scale, they
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
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 Nov 29
2
kernlab's ksvm method freeze
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,
2009 Jul 08
1
ksvm question -- help! line search failed...
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,
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
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)
2007 Aug 08
0
ksvm-kernel
HI
I am new to R.
I have one problem in the predict function of the kernlab.
I want to use ksvm and predict with kernelmatrix (S4 method for signature 'kernelMatrix')
#executing the following sentences
library(kernlab)
# identity kernel
k <- function(x,y) {
n<-length(x)
cont<-0
for(i in 1:n){
if(x[i]==y[i]){
cont<-cont+1
}
}
cont
}
class(k) <-
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
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)
}
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 Jul 07
1
ksvm question -- help! cannot get program to run...
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.
2010 Jun 11
1
Decision values from KSVM
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