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
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
train a multiclass ECOC model composed of SVM models using
For nonlinear classification with big data, train a binary, Gaussian kernel classification
|Classification Learner||Train models to classify data using supervised machine learning|
|Multiclass model for support vector machines or other classifiers|
|Compact multiclass model for support vector machines or other classifiers|
|Cross-validated multiclass model for support vector machines or other classifiers|
|Cross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data|
Create and compare support vector machine (SVM) classifiers, and export trained models to make predictions for new data.
Perform binary classification via SVM using separating hyperplanes and kernel transformations.