similar to: SVM: Graphical representation

Hi, does already Etheora support sound? Bye Javi ______________________________________________ Enviado desde Correo Yahoo! La bandeja de entrada m?s inteligente. -------------- next part -------------- An HTML attachment was scrubbed... URL: http://lists.xiph.org/pipermail/theora/attachments/20080703/57d522fe/attachment.htm

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)

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

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

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

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

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: >

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

Hello I am using support vector machine (from package kernlab) for a classification task (with RBF-Kernel). My data has dozens of variables and I need to identify which variables contribute most to the classification performance. What I did so far is comparing the classification performance (measured for example with the proportion of misclassified cases) of different sets of variables with

I've created an SVM in R using the kernlab package, however it's running incredibly slow (20,000 predictions takes ~45 seconds on win64 R distribution). CPU is running at 25% and RAM utilization is a mere 17% ... it's not a hardware bottleneck. Similar calculations using data mining algorithms in SQL Server analysis services run about 40x faster. Through trial and error, we

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)

Si no puedes pulsar los enlaces, pulsa ??Mostrar el contenido?? e int?ntalo de nuevo. Correo-electr?nico enviado el 10/22/2010 4:53:03 PM, por Adri?n Furlan: Buenos d?as, ?Por fin un sistema inteligente y sencillo para administrar sus contactos! Con UNYK, he reunido todos mis contactos en una sola libreta de direcciones, que se actualiza autom?ticamente. Uno de mis contactos modifica sus

Hello List, I am having a great trouble using svm function in e1071 package. I have 4gb of data that i want to use to train svm. I am using Amazon cloud, my Amazon Machine Image(AMI) has 34.2 GB of memory. my R process was killed several times when i tried to use 4GB of data for svm. Now I am using a subset of that data and it is only 1.4 GB. i remove all unnecessary objects before calling

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) }

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"

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

with pred.pca<-predict(splits[[i]]$pca,trainingData at samples)[,1:nPCs] dframe<-as.data.frame(cbind(pred.pca,class=isExplosive(trainingData,2))); results[[i]]$classifier<-ksvm(class~.,data=dframe,scaled=T,kernel="polydot",type="C-svc", C=C,kpar=list(degree=degree,scale=scale,offset=offset),prob.model=T) and a degree of 5 i get an error of 0 reported by the ksvm

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

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

Hola a todos, estoy intentado entrenar un modelo SVM y me da con el valor de nu=0.1 el siguiente error Error en predict.svm(ret, xhold) : NA/NaN/Inf en llamada a una función externa (arg 8) Según varío el valor de nu a valores menores me da el mismo cuelgue. En cambio si uso valores mayores de nu el ordenador simplemente se cuelga ¿tendrá esto remedio? Uso la librería e1071 ¿existe alguna