Displaying 20 results from an estimated 8000 matches similar to: "Stepwise SVM Variable selection"
2007 Dec 31
1
SVM error
Hi all,
I'm having this error, since I'm working with a data matrix I don't understand what's happening; I've tried several ways to solve this, even working with sparse matrix, but nothing seems to solve it, I've also tried svm (with a simple matrix 3*3 and still got the same error.
> dados<-read.table("b.txt",sep="",nrows=30000)
>
2010 Oct 21
1
SVM classification based on pairwise distance matrix
Dear all,
I am exploring the possibilities for automated classification of my
data. I have successfully used KNN, but was thinking about looking at
SVM (which I did nto use before).
I have a pairwise distance matrix of training observations which are
classified in set classes, and a distance matrix of new observations to
the training ones.
Is it possible to use distance matrices for SVM, and
2008 Mar 02
2
listing components of an object
Is there a method to list the components of an object, instead of looking at the help for that method? Let me be more clear with an example
data(iris)
## tune `svm' for classification with RBF-kernel (default in svm),
## using one split for training/validation set
obj <- tune(svm, Species~., data = iris,
ranges = list(gamma = 2^(-1:1), cost = 2^(2:4)),
2012 Jun 15
1
Sugeestion about tuning of SVM
Dear list
I've a generic question about how to tune an SVM
I'm trying to classify with caret package some population data from a
case-control study . In each column of my matrix there are the SNP
genotypes , in each row there are the individuals.
I correctly splitted my total dataset in training(132 individuals) and test
(50 individuals) (respecting the total observed genotypic
2007 Mar 14
1
tune.svm
I use tune.svm to tune gamma and cost for my training dataset.
I use PC, it runs very slowly. Does anyone know how to make it faster?
Aimin
2009 Jul 12
1
Splitting dataset for Tuning Parameter with Cross Validation
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
2009 Oct 21
2
SVM probability output variation
Dear R:ers,
I'm using the svm from the e1071 package to train a model with the
option "probabilities = TRUE". I then use "predict" with "probabilities
= TRUE" and get the probabilities for the data point belonging to either
class. So far all is well.
My question is why I get different results each time I train the model,
although I use exactly the same data.
2005 Jun 28
2
svm and scaling input
Dear All,
I've a question about scaling the input variables for an analysis with svm (package e1071). Most of my variables are factors with 4 to 6 levels but there are also some numeric variables.
I'm not familiar with the math behind svms, so my assumtions maybe completely wrong ... or obvious. Will the svm automatically expand the factors into a binary matrix? If I add numeric
2010 Dec 03
3
book about "support vector machines"
Dear all,
I am currently looking for a book about support vector machines for
regression and classification and am a bit lost since they are plenty of
books dealing with this subject. I am not totally new to the field and
would like to get more information on that subject for later use with
the e1071 <http://cran.r-project.org/web/packages/e1071/index.html>
package for instance. Does
2009 Aug 12
5
Nominal variables in SVM?
Hi,
The answers to my previous question about nominal variables has lead me
to a more important question.
What is the "best practice" way to feed nominal variable to an SVM.
For example:
color = ("red, "blue", "green")
I could translate that into an index so I wind up with
color= (1,2,3)
But my concern is that the SVM will now think that the values are
2009 Aug 04
1
Save model and predictions from svm
Hello,
I'm using the e1071 package for training an SVM. It seems to be working
well.
This question has two parts:
1) Once I've trained an SVM model, I want to USE it within R at a later
date to predict various new data. I see the write.svm command, but
don't know how to LOAD the model back in so that I can use it tomorrow.
How can I do this?
2) I would like to add the
2015 Dec 09
2
SVM hadoop
Buenos días,
alguien sabe si hay alguna manera de implementar una máquina de soporte
vectorial (svm) con R-hadoop??
Mi interés es hacer procesamiento big data con svm. Se que en R, existen
los paquetes {RtextTools} y {e1071} que permiten hacer svm. Pero no estoy
segura de que el algoritmo sea paralelizable, es decir, que pueda correr en
paralelo a través de la plataforma R-hadoop.
Muchas
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 <-
2015 Dec 10
3
SVM hadoop
Estimados
Un día leí algo en el siguiente hipervínculo, pero nunca lo use.
http://blog.revolutionanalytics.com/2015/06/using-hadoop-with-r-it-depends.html
Javier Rubén Marcuzzi
De: Carlos J. Gil Bellosta
Enviado: miércoles, 9 de diciembre de 2015 14:33
Para: MªLuz Morales
CC: r-help-es
Asunto: Re: [R-es] SVM hadoop
No, no correrán en paralelo si usas los SVM de paquetes como e1071.
No
2003 Jan 31
1
svm regression in R
Hallo,
I have a question concerning SVM regression in R. I intend to use SVMs for feature selection (and knowledge discovery). For this purpose I will need to extract the weights that are associated with my features. I understand from a previous thread on SVM classification, that predictive models can be derived from SVs, coefficiants and rhos, but it is unclear for me how to transfer this
2010 Oct 25
1
online course: SVM in R with Lutz Hamel at statistics.com
Support vector machines (SVMs) have established themselves as
one of the preeminent machine learning models for classification
and regression over the past decade or so, frequently outperforming
artificial neural networks in task such as text mining and
bioinformatics. Dr. Lutz Hamel, author of "Knowledge Discovery with
Support Vector Machines" from Wiley will present his online course
2010 May 05
2
probabilities in svm output in e1071 package
svm.fit<-svm(as.factor(out) ~ ., data=all_h, method="C-classification",
kernel="radial", cost=bestc, gamma=bestg, cross=10) # model fitting
svm.pred<-predict(svm.fit, hh, decision.values = TRUE, probability = TRUE) #
find the probability, but can not find.
attr(svm.pred, "probabilities")
> attr(svm.pred, "probabilities")
1 0
1 0 0
2 0
2011 Feb 21
3
ROC from R-SVM?
*Hi,
*Does anyone know how can I show an *ROC curve for R-SVM*? I understand in
R-SVM we are not optimizing over SVM cost parameter. Any example ROC for
R-SVM code or guidance can be really useful.
Thanks, Angel.
[[alternative HTML version deleted]]
2012 Mar 29
1
TR: [e1071] Load an SVM model exported with write.svm
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Nom : non disponible
URL : <https://stat.ethz.ch/pipermail/r-help/attachments/20120329/cfdd2be3/attachment.pl>
2006 Mar 30
1
Predict function for 'newdata' of different dimension in svm
I am using the "predict" function on a support vector machine (svm)
object, and I don't understand why I can't predict on a dataset with more
observations than the training dataset.
I think this problem is a generic "predict" problem, but I'm not sure.
The original svm was fit on 50 observations.