15-04-2016, 10:38 AM
18-04-2016, 01:56 PM
fuzzy support vector machine matlab code
For greater accuracy and kernel-function choices on low- through medium-dimensional data sets, train a binary SVM model or a multiclass error-correcting output codes (ECOC) model containing SVM binary learners using the Classification Learner app. For greater flexibility, use the command-line interface to train a binary SVM model using fitcsvm or train a multiclass ECOC model composed of binary SVM learners using fitcecoc.
For reduced computation time on high-dimensional data sets that fit in the MATLAB® Workspace, efficiently train a binary, linear classification model, such as a linear SVM model, using fitclinear or train a multiclass ECOC model composed of SVM models using fitcecoc.
For greater accuracy and kernel-function choices on low- through medium-dimensional data sets, train a binary SVM model or a multiclass error-correcting output codes (ECOC) model containing SVM binary learners using the Classification Learner app. For greater flexibility, use the command-line interface to train a binary SVM model using fitcsvm or train a multiclass ECOC model composed of binary SVM learners using fitcecoc.
For reduced computation time on high-dimensional data sets that fit in the MATLAB® Workspace, efficiently train a binary, linear classification model, such as a linear SVM model, using fitclinear or train a multiclass ECOC model composed of SVM models using fitcecoc.