Machine Learning | MATLAB implements support vector machine classification ClassificationSVM parameter setting

Machine learning | MATLAB implements support vector machine classification ClassificationSVM parameter setting

Basic Introduction

ClassificationSVM is a support vector machine (SVM) classifier for one- and two-class learning.The trained ClassificationSVM classifier stores training data, parameter values, prior probabilities, support vectors, and algorithm implementation information.Use these classifiers to perform tasks such as fitting a transfer function of scores to posterior probabilities and predicting labels for new data.

Attribute Description

Use fitcsvm to create a ClassificationSVM object.

Alpha — Trained classifier coefficientsnumeric vector

This property is read-only.
Trained classifier coefficients, specified as an s-by-1 numeric vector.s is the number of support vectors in the training classifier, sum(Mdl.IsSupportVector).
Alpha contains the trained classifier coefficients from the dual problem,