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, 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
For nonlinear classification with big data, train a binary, Gaussian kernel classification model using
|Classification Learner||Train models to classify data using supervised machine learning|
|ClassificationSVM Predict||Classify observations using support vector machine (SVM) classifier for one-class and binary classification|
|Multiclass model for support vector machines (SVMs) and other classifiers|
|Compact multiclass model for support vector machines (SVMs) and other classifiers|
|Cross-validated multiclass ECOC model for support vector machines (SVMs) and other classifiers|
|Cross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data|
|Cross-validated kernel error-correcting output codes (ECOC) model for multiclass classification|
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.
This example shows how to use the ClassificationSVM Predict block for label prediction.
Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)