MATLAB File Help: prtRegressRvmSequential
  prtRegressRvm  Relevance vector machine regression object
    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 = prtRegressRvmSequential;   % 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
    hold off;
    legend('Regression curve','Original Points','Selected Relevant Points','Fitted points',0)
See also
Class Details
Superclasses prtRegressRvm
Sealed false
Construct on load false
Constructor Summary
prtRegressRvmSequential 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 
learningResults Struct with information about the convergence 
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.