Cheat-sheetSDQUADRATIC Quadratic discriminant assuming normal densities P=SDQUADRATIC(DATA,options) INPUT DATA Labeled dataset (with two or more classes) OUTPUT P Quadratic discriminant (Gaussian model per class) OPTIONS 'prior' Class priors (default: use priors from the training set) 'no display' Do not show progress of regularization optimization Regularization: 'reg' Automatic regularization 'reg',R Regularization constant added to diagonal 'test',TS Use a test/validation set TS to evaluate regularization Do not split DATA internally. 'tsfrac',F Fraction of data used to validating error (default: 0.2) DESCRIPTION SDQUADRATIC implements quadratic discriminant assuming Gaussian distributions. For each class a Gaussian model is estimated with full covariance matrix. EXAMPLES p=sdquadratic(data) % Train gaussian model, no regularization p=sdquadratic(data,'reg') % run automatic regularization p=sdquadratic(data,'reg',0.01) % regularize by adding 0.01 on cov.diagonal READ MORE http://perclass.com/doc/guide/classifiers.html#sdquadratic SEE ALSO SDGAUSS, SDLINEAR, SDMIXTURE, SDNMEAN
sdquadratic is referenced in examples: