prtClassRvm  Relevance vector machine classifier
 
    CLASSIFIER = prtClassRvm returns a relevance vector machine classifier
 
    CLASSIFIER = prtClassRvm(PROPERTY1, VALUE1, ...) constructs a
    prtClassRvm object CLASSIFIER with properties as specified by
    PROPERTY/VALUE pairs.
 
    A prtClassRvm object inherits all properties from the abstract class
    prtClass. In addition is has the following properties:
 
    kernels                - A cell array of prtKernel objects specifying
                             the kernels to use
    verbosePlot            - Flag indicating whether or not to plot during
                             training
    verboseText            - Flag indicating whether or not to output
                             verbose updates during training
    learningMaxIterations  - The maximum number of iterations
 
    A prtClassRvm also has the following read-only properties:
 
    learningConverged  - Flag indicating if the training converged
    beta               - The regression weights, estimated during training
    sparseBeta         - The sparse regression weights, estimated during
                         training
    sparseKernels      - The sparse regression kernels, estimated during
                         training
 
    For information on relevance vector machines, please
    refer to the following URL:
 
    http://en.wikipedia.org/wiki/Relevance_vector_machine
 
    By default, prtClassRvm uses the Laplacian approximation as found
    in the paper:
 
    Michael E. Tipping. 2001. Sparse bayesian learning and the
    relevance vector machine. J. Mach. Learn. Res. 1 (September 2001),
 
    The code is based on the algorithm in: 
 
    Herbrich, Learning Kernel Classifiers, The MIT Press, 2002
    http://www.learning-kernel-classifiers.org/
 
    A prtClassRvm object inherits the TRAIN, RUN, CROSSVALIDATE and
    KFOLDS methods from prtAction. It also inherits the PLOT method
    from prtClass.
 
    Example:
 
    TestDataSet = prtDataGenUnimodal;      % Create some test and
    TrainingDataSet = prtDataGenUnimodal;  % training data
    classifier = prtClassRvm;              % Create a classifier
    classifier = classifier.train(TrainingDataSet);    % Train
    classified = run(classifier, TestDataSet);         % Test
    % Plot the results
    subplot(2,1,1);
    classifier.plot;
    subplot(2,1,2);
    [pf,pd] = prtScoreRoc(classified,TestDataSet);
    h = plot(pf,pd,'linewidth',3);
    title('ROC'); xlabel('Pf'); ylabel('Pd');
 
    % Example 2, using a different kernel 
 
    TestDataSet = prtDataGenUnimodal;      % Create some test and
    TrainingDataSet = prtDataGenUnimodal;  % training data
    classifier = prtClassRvm;              % Create a classifier
  
    % Create a prtKernelSet object with a different pair of
    % prtKernels and assign them to the classifier
    kernSet = prtKernelDirect & prtKernelRbf;
    classifier.kernels = kernSet;
 
    classifier = classifier.train(TrainingDataSet);    % Train
    classified = run(classifier, TestDataSet);         % Test
    % Plot
    subplot(2,1,1);
    classifier.plot;
    subplot(2,1,2);
    [pf,pd] = prtScoreRoc(classified,TestDataSet);
    h = plot(pf,pd,'linewidth',3);
    title('ROC'); xlabel('Pf'); ylabel('Pd');