SDPARZEN Parzen density P=SDPARZEN(DATA,options) INPUT DATA SDDATA set or data matrix OUTPUT P Parzen pipeline OPTIONS vector use vector smoothing parameter h smoothing parameter ('scalar' or 'vector') iter number of iterations (def: [] = use maximum smooting difference delta to stop) delta maximum smoothing difference (def: 1e-6) maxsamples limit max number of samples used (default: use all) prior class priors (default: use priors from the training set) gpu run training on GPU (requires parallel comp.toolbox) DESCRIPTION SDPARZEN implements non-parametric Parzen classifier. In training, it estimates smoothing parameter using EM algorithm. By default, Laplace kernel and scalar smoothing parameter is used. Scalar and vector smoothing parameters are supported. Estimation is stoped when delta difference on likelihood is reached. Alternatively, user may specify fixed number of iterations. READ MORE http://perclass.com/doc/guide/classifiers.html#sdparzen
sdparzen
is referenced in examples:
- kb33: Tagging unsure decisions
- kb31: Measuring classifier speed
- kb26: Useful tips for confusion matrices
- kb18: How to protect a trained discriminant against outliers?
- kb16: Visualize the effect of a change of parameters in a trained classifier
- kb15: How to speed up classifiers using the neural network approximation?