search for: rbf

hello , I am new user in R . I have datafile (class = data.frame) which has 825 columns with unique column name i want extract 200 selected column from datafile how can I do this? my datafile look like.. Mi RBN RBF nDB nX 3 2.6225979 0.53132756 -0.80599902 -1.4471864 -0.5705269 10 0.4818746 -1.72143092 -2.19579027 2.0118824 -0.5705269 12 2.8519611 1.88298265 0.09614617 0.6282549 -0.5705269 20 0.6347834 -0.36977583 0.63255683 1.3200687 -0.5705269 I want to extract data with unique...

...ME)', error=2 Could not load 'ODBC.DLL' required by '(AME)', error=2 fixme:storage:StgCreateDocfile Transacted mode not implemented. fixme:storage:StorageImpl_Commit (1): stub! fixme:storage:StorageImpl_Commit (1): stub! fixme:file:SetFileAttributesW (L"C:\\Config.Msi\\3367.rbf"):8000 attribute(s) not implemented. fixme:file:SetFileAttributesW (L"C:\\Config.Msi\\3368.rbf"):8000 attribute(s) not implemented. fixme:file:SetFileAttributesW (L"C:\\Config.Msi\\3369.rbf"):8000 attribute(s) not implemented. fixme:file:SetFileAttributesW (L"C:\\Confi...

Hi all, sorry for my english, but I don't speak yours language. I'm trying to use bart() and rbf(). The package I'm using now is "BayesTree" and "neural", respectively. I could create the models, but I can't predict my test data. Does anyone have such an experience? Any advice is appreciated! Thank you in advanced!. Andr? -- View this message in context: h...

Hello everybody, I'm looking for a way to build an RBF classification network with R but I can't find any. I know there is the 'neural' package, but apparently the RBF networks I can build with that are for approximation tasks only. Is there any package I can use to build an RBF network for a classification task? I've also looked on CRA...

...orm a cross validation, -say 10fold- for a given data set and given model ? 2.) Second question What is the correct syntax for the parametrization of e.g. Kernel classifiers interface m1 <- SMO(Species ~ ., data = iris, control = Weka_control(K="weka.classifiers.functions.supportVector.RBFKernel",G=0.1)) m2 <- SMO(Species ~ ., data = iris, control = Weka_control(K="weka.classifiers.functions.supportVector.RBFKernel",G=1.0)) > m1 SMO Kernel used: RBF kernel: K(x,y) = e^-(0.01* <x-y,x-y>^2) ## should be: RBF kernel: K(x,y) = e^-(0.1* <x-y...

...;, (null), (nil)): stub fixme:imagehlp:BindImageEx (0, "C:\\Program Files\\Microsoft Office\\Office\\1033\\WW9INTL.DLL", "C:\\Program Files\\Microsoft Office\\Office\\1033\\;;C:\\WINDOWS\\SYSTEM", (null), (nil)): stub fixme:file:SetFileAttributesW (L"C:\\Config.Msi\\42a6076.rbf"):8000 attribute(s) not implemented. fixme:file:SetFileAttributesW (L"C:\\Config.Msi\\42a6077.rbf"):8000 attribute(s) not implemented. fixme:file:SetFileAttributesW (L"C:\\Config.Msi\\42a6078.rbf"):8000 attribute(s) not implemented. fixme:file:SetFileAttributesW (L"C:\...

...a simple matrix 3*3 and still got the same error. > dados<-read.table("b.txt",sep="",nrows=30000) > dados1<-as.matrix(dados[,-1],nrows=30000,ncol=13,type=numeric) > dados2<-as.vector(dados[,1]) > model<-svm(scale=TRUE,type=C,dados[,-1],y=dados[,1],kernel=RBF) Error in as.character(x) : cannot coerce to vector > model<-svm(scale=TRUE,type=C,dados1,y=dados2,kernel=RBF) Error in as.character(x) : cannot coerce to vector Best regards, Pedro Marques

...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 <- ksvm(Class~.,data=promotergene,kernel="rbfdot",C=10,cross=5)# works fine with the rbf kernel gene.string <- ksvm(Class~.,data=promotergene,kernel="stringdot",C=10,cross=5) # this give the following error #Error in match.arg(kernel, c("rbfdot", "polydot...

..."and" "\xc1d\xe1m" [4] "graphical" "interface" "MLP" [7] "Nagy" "networks" "Networks" [10] "neural" "Neural" "RBF" [13] "<sodzsu at yahoo.com>" "user" "with" [16] "and" "\xc1d\xe1m" "graphical" [19] "interface" "MLP" > I need to get rid off text[3,17] ! I have this kind...

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 once for each choice? And why? Thanks and regards!

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)), tunecontrol = tune.control(sampling = "fix") ) ## alternatively: ## obj &...

I have a data set with about 30,000 training cases and 103 variable. I've trained an SVM (using the e1071 package) for a binary classifier {0,1}. The accuracy 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 variabl...

...0000 9 0.05920000 0.06936667 0.06780000 0.08486667 10 0.05993333 0.07300000 0.06516667 0.08596667 11 0.06316667 0.07266667 0.07033333 0.09040000 12 0.06326667 0.07743333 0.07246667 0.09353333 > _____________________________________________________________________________________________ > RBF <- aov(ThetaExpT$ThetaV ~ factor(ThetaExpT$Block) + > factor(ThetaExpT$Week):factor(ThetaExpT$Treatment) ) > anova(RBF) Analysis of Variance Table Response: ThetaExpT$ThetaV Df Sum Sq Mean Sq factor(ThetaExpT$Block)...

...number of samples. Why? Here I provide e.g. with Iris data in R, keep reducing prediction cases one-by-one, you will see the discrepancy I am talking about. In my own data, this discrepancy between odd and even number of cases is enhanced by a huge factor. Thanks, Parmee iris <- unique(iris) rbf <- rbfdot(0.5) lssvm> k <- kernelMatrix(rbf, as.matrix(iris[,-5])) lssvm> klir <- lssvm(k, iris[, 5]) lssvm> pre <- predict(klir, k) > ktest <- as.kernelMatrix(k[1:148,]) > pretest <- predict(klir, ktest) > table(pretest,iris[1:148,5]) pretest set...

Dear Forum, I have installed and used various Weka functions in R - both already available interfaces or created ones via make_Weka_classifier - without any trouble. However, the RBFNetwork (RBF Neural Network) function is one that I have not been able to call. I tried creating the R interface using RBF<- make_Weka_classifier("weka/classifiers/functions/RBFNetwork"), and the following is the error I get: Error in .jnew(name) : java.lang.ClassNotFoundException Do...

...m$SV x.scaled = scale(x, m$x.scale[[1]], m$x.scale[[2]]) t(w %*% t(as.matrix(x.scaled))) - m$rho but this is wrong If i get this right what the answer should be is f(x) = w%*%phi(x)+b but the question is what exactly is phi (by default) and how do you do this if you cannot get phi in closer form (rbf kernel?). It seems like the answer would lie with the dual representation of the above, i think it is f(x) = sum_{support vectors}{(a_i-a*^{*}_{i})K(x_{i},x)}+b but how do you get the a variables everything else. An actual formula would be greatly appreciated! Thanks! -Andrei [[alternative HTML...

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

...outcome should be in a column called '.outcome'. and gives the following examples: ------------------------------------------------------------ createGrid("rda", 4) createGrid("lm") createGrid("nnet") ## data needed for SVM with RBF: ## Not run: tmp <- iris names(tmp)[5] <- ".outcome" head(tmp) createGrid("svmRadial", data = tmp, len = 4) ## End(Not run) What I am doing wrong? Also, what is the connection between len above and tuneLength in the argument for train? T...

...ach 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 frequencies and the % of cases and controls) After training (with radial RBF function) I have an accuracy of the best model of 76% but applying the model to my test dataset the accuracy decreases to 52%. Obviously i expected the decrease but this appear to be quite big in my opinion. I manually checked the predictions for my test dataset and some cases that have no risk al...

I have a compilation problem on FC2, 2xXeon box. The following dialogue output from the end of the compilation illustrates: [very large snipping sound ...] * DONE (cluster) begin installing recommended package foreign make[2]: *** [foreign.ts] Error 1 make[2]: Leaving directory `/usr/src/redhat/SOURCES/R-2.0.1/src/library/Recommended' make[1]: *** [recommended-packages] Error 2 make[1]: