Support Vector Machine 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, 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 Learner | Train models to classify data using supervised machine learning |
Blocks
ClassificationSVM Predict | Classify observations using support vector machine (SVM) classifier for one-class and binary classification (Since R2020b) |
ClassificationECOC Predict | Classify observations using error-correcting output codes (ECOC) classification model (Since R2023a) |
ClassificationLinear Predict | Classify observations using linear classification model (Since R2023a) |
ClassificationKernel Predict | Classify observations using Gaussian kernel classifier for binary classification (Since R2024b) |
IncrementalClassificationLinear Predict | Classify observations using incremental linear classification model (Since R2023b) |
IncrementalClassificationLinear Fit | Fit incremental linear binary classification model (Since R2023b) |
IncrementalClassificationECOC Predict | Classify observations using incremental ECOC classification model (Since R2024a) |
IncrementalClassificationECOC Fit | Fit incremental ECOC classification model (Since R2024a) |
IncrementalClassificationKernel Fit | Fit incremental kernel classification model (Since R2024b) |
IncrementalClassificationKernel Predict | Classify observations using incremental kernel classification model (Since R2024b) |
Detect Drift | Update drift detector states and drift status with new data (Since R2024b) |
Update Metrics | Update performance metrics in incremental learning model given new data (Since R2023b) |
Functions
Classes
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.
- Predict Class Labels Using ClassificationSVM Predict Block
This example shows how to use the ClassificationSVM Predict block for label prediction in Simulink®.
- Predict Class Labels Using ClassificationLinear Predict Block
This example shows how to use the ClassificationLinear Predict block for label prediction in Simulink®. (Since R2023a)
- Predict Class Labels Using ClassificationECOC Predict Block
Train an ECOC classification model, and then use the ClassificationECOC Predict block for label prediction. (Since R2023a)
Related Information
- Signal Classification Using Wavelet-Based Features and Support Vector Machines (Wavelet Toolbox)
- Wavelet Time Scattering Classification of Phonocardiogram Data (Wavelet Toolbox)