MATLAB File Help: prtClassRvmFigueiredo
  prtClassRvmFigueiredo  Relevance vector machine classifier using a Jefferey's prior
     CLASSIFIER = prtClassRvmFigueiredo returns a relevance vector 
     machine classifier.
     CLASSIFIER = prtClassRvmFigueiredo(PROPERTY1, VALUE1, ...) constructs a
     prtClassRvm object CLASSIFIER with properties as specified by
     PROPERTY/VALUE pairs.
     A prtClassRvmFigueiredo object inherits all properties from the
     abstract class prtClass. In addition is has the following
     kernels            - A cell array of prtKernel objects specifying
                          the kernels to use
     verbosePlot        - Flag indicating whether or not to plot during
     verboseText        - Flag indicating whether or not to output
                          verbose updates during training
     learningMaxIterations  - The maximum number of iterations
     A prtClassRvmFigeiredo 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
     sparseKernels      - The sparse regression kernels, estimated during
    For more informatoin on the Figueiredo algorithm, please refer to
    the following reference:
    M. Figueiredo, Adaptive sparseness for supervised learning, 
    IEEE PAMI, vol. 25, no. 9 pp.1150-1159, September 2003.  
    Training using the Figueiredo algorithm can provide faster
    and more robust convergence under some circumstances.
    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
     %    % Create a classifier with verbose plotting
    classifier = prtClassRvmFigueiredo('verbosePlot',true); 
    classifier = classifier.train(TrainingDataSet);    % Train
    classified = run(classifier, TestDataSet);         % Test
    % Plot the results
    % figure
    [pf,pd] = prtScoreRoc(classified,TestDataSet);
    h = plot(pf,pd,'linewidth',3);
    title('ROC'); xlabel('Pf'); ylabel('Pd');
See also
Class Details
Superclasses prtClassRvm
Sealed false
Construct on load false
Constructor Summary
prtClassRvmFigueiredo Relevance vector machine classifier using a Jefferey's prior 
Property Summary
beta Regression weights 
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
internalDecider Optional prtDecider object for making decisions 
isCrossValidateValid True 
isNativeMary False 
isSupervised True 
isTrained Indicates if prtAction object has been trained. 
kernels The kernels to be used 
learningConverged Flag indicating whether or not training convereged 
learningConvergedTolerance Learning tolerance;  
learningMaxIterations The maximum number of iterations 
learningRelevantTolerance Tolerance below which a kernel is marked as irrelevant and removed 
name Relevance Vector Machine 
nameAbbreviation RVM 
sparseBeta Sparse Beta 
sparseKernels Sparse Kernel array 
twoClassParadigm Whether the classifier retures one output (binary) or two outputs (m-ary) when there are only two unique class labels 
userData User specified data 
verbosePlot Whether or not to plot during training 
verboseStorage Specifies whether or not to store the training prtDataset. 
verboseText Whether or not to display text during training 
Method Summary
  crossValidate Cross validate prtAction using prtDataSet and cross validation keys. 
  get get the object properties 
  kfolds Perform K-folds cross-validation of prtAction 
  optimize Optimize action parameter by exhaustive function maximization. 
  plot Plot output confidence of the prtClassRvm object 
  run Run a prtAction object on a prtDataSet object. 
  set set the object properties 
  train Train a prtAction object using training a prtDataSet object.