Cheat-sheetSDLINEAR linear discriminant assuming normal densities P=SDLINEAR(DATA,options) INPUT DATA labeled dataset OUTPUT P linear discriminant (Gaussian model per class, pooled covariance matrix) 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 SDLINEAR implements linear discriminant assuming that all classes are Gaussian with the same covariance matrix. EXAMPLES p=sdlinear(data) % Train gaussian model, no regularization p=sdlinear(data,'reg') % run automatic regularization p=sdlinear(data,'reg',0.01) % regularize by adding 0.01 on cov.diagonal READ MORE http://perclass.com/doc/guide/classifiers.html#sdlinear SEE ALSO SDQUADRATIC, SDNMEAN, SDGAUSS
sdlinear is referenced in examples: