MATLAB File Help: prtRegressRvm
  prtRegressRvm  Relevance vector machine regression object
    REGRESS = prtRegressRvm returns a prtRegressRvm object
     REGRESS = prtRegressRVM(PROPERTY1, VALUE1, ...) constructs a
     prtRegressRvm object REGRESS with properties as specified by
     PROPERTY/VALUE pairs.
     A prtRegressRvm object inherits all properties from the prtRegress
     class. In addition, it has the following properties:
    SetAccess = public:
     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 display
                          a message during training
    SetAccess = private/protected:
     learningConverged  - Flag indicating if the training converged
     learningResults    - Struct with information about the convergence
     beta               - The weights on each of the kernel elements;
                          learned during training
     Sigma              - The learned covariance
     sparseBeta         - The weights on the retained kernel elements;
                          learned durning training
     sparseKernels      - The retained kernels
    This code is based on:
        Michael E Tipping, Sparse bayesian learning and the relevance 
    vector machine, The Journal of Machine Learning Research, Vol 1.
    Also see
    A prtRegressionRvm object inherits the PLOT method from the
    prtRegress object, and the TRAIN, RUN, CROSSVALIDATE and KFOLDS
    methods from the prtAction object.
    dataSet = prtDataGenNoisySinc;           % Load a prtDataRegress
    dataSet.plot;                    % Display data
    reg = prtRegressRvm;             % Create a prtRegressRvm object
    reg = reg.train(dataSet);        % Train the prtRegressRvm object
    reg.plot();                      % Plot the resulting curve
    dataSetOut =;   % Run the regressor on the data
    hold on;
    plot(dataSet.getX,dataSetOut.getX,'c.') % Plot, overlaying the
                                            % fitted points with the 
                                            % curve and original data
    legend('Regression curve','Original Points','Kernel Locations Used',0)
See also
Class Details
Superclasses prtRegress
Sealed false
Construct on load false
Constructor Summary
prtRegressRvm Relevance vector machine regression object 
Property Summary
Sigma Estimated in training 
beta Estimated in training 
dataSet The training prtDataSet, only stored if verboseStorage is true.  
dataSetSummary Structure that summarizes prtDataSet. 
isCrossValidateValid True 
isSupervised True 
isTrained Indicates if prtAction object has been trained. 
learningConverged Whether or not the training converged 
name Relevance Vector Machine 
nameAbbreviation RVM 
plotOptions Plotting Options 
sigma2 Estimated in training 
sparseBeta Estimated in training 
sparseKernels Estimated in training  
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 plot 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 the prtRegress 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.