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Support Vector Machine Classification

Support vector machines for binary or multiclass classification

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 nonlinear classification with big data, train a binary, Gaussian kernel classification model using fitckernel.

Apps

Classification LearnerTrain models to classify data using supervised machine learning

Functions

fitcsvmTrain binary support vector machine classifier
fitSVMPosteriorFit posterior probabilities
predictPredict labels using support vector machine classification model
templateSVMSupport vector machine template
fitclinearFit linear classification model to high-dimensional data
predictPredict labels for linear classification models
templateLinearLinear classification learner template
fitckernelFit Gaussian kernel classification model using feature expansion for big data
predictPredict labels for Gaussian kernel classification model
fitcecocFit multiclass models for support vector machines or other classifiers
predictPredict labels using multiclass, error-correcting output codes model
templateECOCError-correcting output codes learner template

Classes

ClassificationSVMSupport vector machine for binary classification
CompactClassificationSVMCompact support vector machine for binary classification
ClassificationPartitionedModelCross-validated classification model
ClassificationLinearLinear model for binary classification of high-dimensional data
ClassificationPartitionedLinearCross-validated linear model for binary classification of high-dimensional data
ClassificationKernelGaussian kernel classification model using feature expansion for big data
ClassificationECOCMulticlass model for support vector machines or other classifiers
CompactClassificationECOCCompact multiclass model for support vector machines or other classifiers
ClassificationPartitionedECOCCross-validated multiclass model for support vector machines or other classifiers
ClassificationPartitionedLinearECOCCross-validated linear error-correcting output codes model for multiclass classification of high-dimensional data

Topics

Train Support Vector Machines Using Classification Learner App

Create and compare support vector machine (SVM) classifiers, and export trained models to make predictions for new data.

Support Vector Machines for Binary Classification

Perform binary classification via SVM using separating hyperplanes and kernel transformations.

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