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https://www.mathworks.com/help/stats/support-vector-machine-classification.html
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.fitcsvm: Train binary support vector machine (SVM) classifier
https://www.mathworks.com/discovery/support-vector-machine.html
A support vector machine (SVM) is a supervised learning algorithm that can be used for binary classification or regression. Support vector machines are popular in applications such as natural language processing, speech and image recognition, and computer vision.. A support vector machine constructs an optimal hyperplane as a decision surface such that the margin of separation between …
http://www.isis.ecs.soton.ac.uk/resources/svminfo/
MATLAB Support Vector Machine Toolbox The toolbox provides routines for support vector classification and support vector regression. A GUI is included which allows the visualisation of simple classification and regression problems. (The MATLAB optimisation toolbox, or an alternative quadratic programming routine is required.)
https://la.mathworks.com/discovery/support-vector-machine.html
Una máquina de vectores de soporte (SVM) es un algoritmo de aprendizaje supervisado que se puede emplear para clasificación binaria o regresión. Las máquinas de vectores de soporte son muy populares en aplicaciones como el procesamiento del lenguaje natural, el habla, el reconocimiento de imágenes y la visión artificial.. Una máquina de vectores de soporte construye un hiperplano ...
https://www.uea.ac.uk/computing/matlab-svm-toolbox
This is a beta version of a MATLAB toolbox implementing Vapnik's support vector machine, as described in [1]. Training is performed using the SMO algorithm, due to Platt [2], implemented as a mex file (for speed). Before you use the toolbox you need to run the compilemex script to recompile them (if ...
https://uk.mathworks.com/help/stats/regressionsvm-class.html
RegressionSVM is a support vector machine (SVM) regression model. Box constraints for dual problem alpha coefficients, stored as a numeric vector containing n elements, where n is the number of observations in X (Mdl.NumObservations).. The absolute value of the dual coefficient Alpha for observation i cannot exceed BoxConstraints(i).
https://jp.mathworks.com/discovery/support-vector-machine.html
A support vector machine (SVM) is a supervised learning algorithm that can be used for binary classification or regression. Support vector machines are popular in applications such as natural language processing, speech and image recognition, and computer vision.. A support vector machine constructs an optimal hyperplane as a decision surface such that the margin of separation between …
https://fr.mathworks.com/help/stats/support-vector-machine-regression.html
Support vector machines for regression models. For greater accuracy on low- through medium-dimensional data sets, train a support vector machine (SVM) model using fitrsvm.. For reduced computation time on high-dimensional data sets, efficiently train a linear regression model, such as a linear SVM model, using fitrlinear.fitrsvm: Fit a support vector machine regression model
https://de.mathworks.com/help/stats/support-vector-machine-classification.html
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.fitcsvm: Train binary support vector machine (SVM) classifier
https://it.mathworks.com/help/stats/understanding-support-vector-machine-regression.html
Understanding Support Vector Machine Regression Mathematical Formulation of SVM Regression Overview. Support vector machine (SVM) analysis is a popular machine learning tool for classification and regression, first identified by Vladimir Vapnik and his colleagues in 1992.SVM regression is considered a nonparametric technique because it relies on kernel functions.
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