search for: svms

Hi, Has anyone implemented techniques for model selection in support vector machines in R? I have seen recursive feature wrappers and other methods in the literature, but I can't find implementations. Thanks in advance, Max Kuhn BD Diagnostic Systems ----------------------------------------- This message is intended only for the designated recipient(s...{{dropped}}

Hi again! This might be more of a statistical question, but anyway: If i train several support vector machines with different degrees of polynomials, and as result, get that higher degrees not only have a higher test error, but also a higher in-sample error, why is that? I would assume i should get an in-sample error lower or at least the same as the linear case. i'm worried i did

...al basis: exp(-gamma*|u-v|^2). You should be able to see a PNG of the resulting plot here: http://www.flickr.com/photos/60118409 at N00/91835679/ The problem is that that's not good enough. I want a better fit. I think I can get one, I just don't know how. There's a paper on Proximal SVMs that claims a better result. To the best of my knowledge, PSVMs should not outperform SVMs, they are merely faster to compute. You can find the paper (with the picture of their SVM) on citeseer: http://citeseer.ifi.unizh.ch/cachedpage/515368/5 @misc{ fung-proximal, author = "G. Fung and O. M...

Hi everybody, I am new in e1071 and with SVMs. I am trying to understand the performance of SVMs but I face with a situation that I thought as not meaningful. I added the R code for you to see what I have done. /set.seed(1234) data <- data.frame( rbind(matrix(rnorm(1500, mean = 10, sd = 5),ncol = 10), matrix(rnorm(1500, mean = 5, sd = 5...

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 S...

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 variables outside the range of 0 to 1 do I have to scale them to have 0 to 1 range? thanks a lot for help, +kind regards, Arne

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 t...

...ld be keyed off depending whether mc.set.seed is logical, preserving the current behaviour, or numerical, using the value in a call to set.seed. Does this make sense? If you wonder how I came up with the idea: I spent a couple of hours debugging "unstable" results from parallel tuning of svms, which was caused by the parallel execution. In my case I can simply do the set.seed in the FUN argument function, but that may not be always the case. Ivan [[alternative HTML version deleted]]

Hello, I am doing intensive tests on SVMs parameter selection. Once a while I got the error: Error in save.image(): image could not be renamed and is left in .RDataTmp1 I cannot use the information saves in .RDataTmp1. When that happens I loose several hours of tests. It happens, ussualy when the computer is locked, i.e., there is not oth...

...lado, los trucos habituales para hacer algo "parecido" a SVM o "basado" en SVM pero que no sea SVM. Si es que eso te vale. Puedes probar a hacerlo con mllib (sobre Spark), como aquí <http://spark.apache.org/docs/latest/mllib-linear-methods.html#linear-support-vector-machines-svms>. ¡Pero no lo he probado nunca! Un saludo, Carlos J. Gil Bellosta http://www.datanalytics.com El 9 de diciembre de 2015, 13:15, MªLuz Morales <mlzmrls en gmail.com> escribió: > Buenos días, > > alguien sabe si hay alguna manera de implementar una máquina de soporte > vector...

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

...curacy isn't great. I used a grid search over the C and G parameters with an RBF kernel to find the best settings. I remember that for least squares, R has a nice stepwise function that will try combining subsets of variables to find the optimal result. Clearly, this doesn't exist for SVMs as a built in function. As an experiment, I simply grabbed the first 50 variables and repeated the training/grid search procedure. The results were significantly better. Since the date is VERY noisy, my guess is that eliminating some of the variables eliminated some noise that resulted in bet...

...ur data are linearly separable. To see this (if you didn't already know), try library(lattice) splom(~x, groups = y) and look at the first row. If you are trying to do classification, there are a few methods that would choke on this (logistic regression) and a few that won't (trees, svms etc). I would guess that bruto is in the latter group. However, if you are try to do classification, try using bruto via fda: > tmp <- cbind(x, factor(y)) > > fdaFit <- fda(y2~., tmp) > fdaFit Call: fda(formula = y2 ~ ., data = tmp) Dimension: 1...

...le. >>> > >>> > >>> > >>> > Puedes probar a hacerlo con mllib (sobre Spark), como aquí >>> > >>> > < >>> > >>> http://spark.apache.org/docs/latest/mllib-linear-methods.html#linear-support-vector-machines-svms >>> > >. >>> > >>> > ¡Pero no lo he probado nunca! >>> > >>> > >>> > >>> > Un saludo, >>> > >>> > >>> > >>> > Carlos J. Gil Bellosta >>> > >>>...

...forests or support vector machines. The workshop tries to bring together the machine learning view with the statistical view on ensemble methods. By now, the following talks have been scheduled: * Gilles Blanchard: Consistency results for Boosting * Peter Buehlmann: tba * Gunnar Raetsch: Boosting SVMs: a new way for multiple kernel learning * Koji Tsuda: Matrix exponentiated Gradients Participants can attend the workshop free of charge, the number of participants is restricted to 30 persons. For registration and submission of abstracts, please send an email message to Gunnar.Raetsch at tuebing...

...ave 4 independent variables, I'd expect to have four coefficients plus a threshold, with 4 total degrees of freedom. But the only numeric vectors of length 4 in the result are the scaling and center, and those are done before the fitting so each one has zero mean and unit variance. I know svms don't need to put every point through the kernel function, and can even handle infinite dimensional kernels. But don't they need to compute the coefficients? Best, Martin

...<- prediction(hiv.svm$predictions, hiv.svm$labels) perf.svm <- performance(pred.svm, 'tpr', 'fpr') pred.nn <- prediction(hiv.nn$predictions, hiv.svm$labels) perf.nn <- performance(pred.nn, 'tpr', 'fpr') plot(perf.svm, lty=3, col="red",main="SVMs and NNs for prediction of HIV-1 coreceptor usage") now I see some dotted red ROC curves. Now after the next command: plot(perf.nn, lty=3, col="blue",add=TRUE) there happens exactly the same as if i would call: plot(perf.nn, lty=3, col="blue") I now see only some dotted b...

...ier courses are not a prerequisite for this new course. In this course we emphasize the tools useful for tackling modern-day data analysis problems. We focus on both "tall" data ( N>p where N=#cases, p=#features) and "wide" data (p>N). The tools include gradient boosting, SVMs and kernel methods, random forests, lasso and LARS, ridge regression and GAMs, supervised principal components, and cross-validation. We also present some interesting case studies in a variety of application areas. All our examples are developed using the S language, and most of the procedures we...

...ier courses are not a prerequisite for this new course. In this course we emphasize the tools useful for tackling modern-day data analysis problems. We focus on both "tall" data ( N>p where N=#cases, p=#features) and "wide" data (p>N). The tools include gradient boosting, SVMs and kernel methods, random forests, lasso and LARS, ridge regression and GAMs, supervised principal components, and cross-validation. We also present some interesting case studies in a variety of application areas. All our examples are developed using the S language, and most of the procedures we...

Dear All, I am using "Caret"package for SVM regression and elastic net regression . I can get the final fiited vs observed values. How can I get the coefficients? Any ideas? Thanks Alex [[alternative HTML version deleted]]