search for: lssvm

Hello, I have noticed a bug with LSSVM implementation in R. It could be a bug with the LSSVM itself that causes this problem. I thought I should post this message to see if anyone else is familiar with this problem and explain why the result is different for odd and even number of cases. Once the hyperplane is found using LSSVM, the p...

Hello, In my ongoing quest to develop a "best" model, I'm testing various forms of SVM to see which is best for my application. I have been using the SVM from the e1071 library without problem for several weeks. Now, I'm interested in RVM and LSSVM to see if I get better performance. When running RVM or LSSVM on the exact same data as the SVM{e1071}, I get an error that I don't understand: Error in .local(x, ...) : kernel must inherit from class 'kernel' Does this make sense to anyone? Can you suggest how to resolve this? Th...

...of single soil properties of a huge data set (3000 samples). There are for the eps-regression using the radial basis kernel three optimization parameters needed. To make things easier (using only two optimization parameters and not loosing performance) I wanted to use LS SVM regression (lssvm{kernlab}). But it looks to me that it is not yet implemented. At least I got error messages, which I could not find a solution for (Error in if (n !_dim(y)[1] stop ("Labels y and data x dont match"). Otherwise I could not find another LSSVM regression implementation in R, or is the...

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"

...) : 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 method o onlearn() : Kernel-based Online Learning algorithms for class...

...ree, logistic model 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 addi...

...) : 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 method o onlearn() : Kernel-based Online Learning algorithms for class...

...ree, logistic model 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 addi...