MATLAB File Help: prtClassRvmSequential
  prtClassRvmSequential  Relevance vector machine classifier using sequential training
    CLASSIFIER = prtClassRvmSequential returns a relevance vector
    machine classifier based using sequential training.
    CLASSIFIER = prtClassRvmSequential(PROPERTY1, VALUE1, ...)
    constructs a prtClassRvmSequential object CLASSIFIER with properties as
    specified by PROPERTY/VALUE pairs.
     A prtClassRvmSequential 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
     verboseText            - Flag indicating whether or not to output
                              verbose updates during training
     learningMaxIterations  - The maximum number of iterations
     A prtClassRvmSequential 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 information on the algorithm and the above properties, see
    the following reference:
    Tipping, M. E. and A. C. Faul (2003). Fast marginal likelihood
    maximisation for sparse Bayesian models. In C. M. Bishop and B. J.
    Frey (Eds.), Proceedings of the Ninth International Workshop on
    Artificial Intelligence and Statistics, Key West, FL, Jan 3-6.
    prtClassRvmSequential is most useful for datasets with a large
    number of observations for which the gram matrix can not be held in
    memory. The sequential RVM training algorithm is capable of
    operating by generating necessary portions of the gram matrix when
    needed. The size of the generated portion of the gram matrix is
    determined by the property, largestNumberOfGramColumns. Sequential
    RVM training will attempt to generate portions of the gram matrix
    that are TraingData.nObservations x largesNumberofGramColums in
    size. If the entire gram matrix is this size or smaller it need
    only be generated once. Therefore if the entire gram matrix can be
    stored in memory, training is much faster. For quickest operation,
    largestNumberOfGramColumns should be set as large as possible
    without exceeding RAM limitations.
     TestDataSet = prtDataGenUnimodal;      % Create some test and
     TrainingDataSet = prtDataGenUnimodal;  % training data classifier
     classifier = prtClassRvmSequential('verbosePlot',true); % Create a classifier
     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');
See also
Class Details
Superclasses prtClassRvm
Sealed false
Construct on load false
Constructor Summary
prtClassRvmSequential Relevance vector machine classifier using sequential training 
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;  
learningFactorRemove Remove kernels during train? 
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.