Using Support Vector Machines As with any supervised learning model, you first train a support vector machine, and then cross validate the classifier. Use the trained machine to classify (predict) new data. In addition, to obtain satisfactory predictive accuracy, you can use various SVM kernel fu...
This MATLAB function returns the classification error (see Classification Loss), a scalar representing how well the trained support vector machine (SVM) classifier (SVMModel) classifies the predictor data in table Tbl compared to the true class labels in
trains or cross-validates a support vector machine (SVM) model for one-class and two-class (binary) classification on a low-dimensional or moderate-dimensional predictor data set.supports mapping the predictor data using kernel functions, and supports sequential minimal optimization (SMO), iterative ...
This MATLAB function returns a support vector machine (SVM) learner template suitable for training classification or regression models.
A support vector machine is a supervised machine learning algorithm that finds an optimal hyperplane that separates data of different classes. Get code examples.
Support vector machines for binary or multiclass classificationFor 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 Learne...
Train a support vector machine regression model using the abalone data from the UCI Machine Learning Repository. Download the data and save it in your current folder with the name'abalone.csv'. url ='https://archive.ics.uci.edu/ml/machine-learning-databases/abalone/abalone.data'; websave('ab...
Support vector machines for regression modelsFor 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 mo...
fitrsvm trains or cross-validates a support vector machine (SVM) regression model on a low- through moderate-dimensional predictor data set. fitrsvm supports mapping the predictor data using kernel functions, and supports SMO, ISDA, or L1 soft-margin minimization via quadratic programming for object...
MATLAB Coder Statistics and Machine Learning Toolbox Train a support vector machine (SVM) model using a partial data set and create a coder configurer for the model. Use the properties of the coder configurer to specify coder attributes of the SVM model parameters. Use the object function ...