search for: 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 .local(object, ...) : test vector does not match model !" I realize that maybe kernlab does not support the kind of operation I'm trying to...

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"

...g package for R. kernlab includes the following functions: o ksvm() : Support Vector Machines for classification, regression, novelty detection, native multi-class classification, support for class-probability output and confidence intervals in regression. o gausspr() : Gaussian Processes for classification and regression o lssvm() : Least Squares Support Vector Machines for classification o rvm() : Relevance Vector Machines for regression o specc() : Spectral Clustering o kkmeans() : Kernel k-means clustering o ranking() : Kernel-based ranking metho...

...l trees), mars (via earth), boosted models (ada, gbm, blackboost, glmboost, gamboost, logitboost), bagged models (trees, earth, fda), randomforests (randomforest and cforest), rule-based models (Ripper and M5 prime), discriminant models (lda, fda, rda, ssda, slda), kernel methods (lssvm, ksvm, rvm, gausspr), nnet, nnet with initial pca step, multinom, pls, plsda, gpls, nearest shrunken centroids, the lasso, the elastic net, supervised pca, knn, lvq and NaiveBayes. Recent changes include: - Estimation of class probabilities from PLS discriminant analysis using Bayes rule (in addition to softmax) - A...

...g package for R. kernlab includes the following functions: o ksvm() : Support Vector Machines for classification, regression, novelty detection, native multi-class classification, support for class-probability output and confidence intervals in regression. o gausspr() : Gaussian Processes for classification and regression o lssvm() : Least Squares Support Vector Machines for classification o rvm() : Relevance Vector Machines for regression o specc() : Spectral Clustering o kkmeans() : Kernel k-means clustering o ranking() : Kernel-based ranking metho...

...l trees), mars (via earth), boosted models (ada, gbm, blackboost, glmboost, gamboost, logitboost), bagged models (trees, earth, fda), randomforests (randomforest and cforest), rule-based models (Ripper and M5 prime), discriminant models (lda, fda, rda, ssda, slda), kernel methods (lssvm, ksvm, rvm, gausspr), nnet, nnet with initial pca step, multinom, pls, plsda, gpls, nearest shrunken centroids, the lasso, the elastic net, supervised pca, knn, lvq and NaiveBayes. Recent changes include: - Estimation of class probabilities from PLS discriminant analysis using Bayes rule (in addition to softmax) - A...

...; I am trying to do a time series prediction using Gaussian Processes (need > to try with different kernel functions) using R. > > I am using kernlab package to do so. But I am not sure how do I predict > for new data.!! > > I used following to train the model : > > >gp = gausspr( t, weekdays1_vector_t_t_trunc, kernel="laplacedot", > scaled=FALSE ) > > Then, I predict using predict function : > >pred = predict(gp, t_new) > > > But, when I plot the data, the model seems to fit quite ok, but prediction > is nowhere close to the actual data...