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 http://en.wikipedia.org/wiki/Relevance_vector_machine A prtRegressionRvm object inherits the PLOT method from the prtRegress object, and the TRAIN, RUN, CROSSVALIDATE and KFOLDS methods from the prtAction object. Example: 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 = reg.run(dataSet); % 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)
Superclasses | prtRegressRvm |
Sealed | false |
Construct on load | false |
prtRegressRvmSequential | prtRegressRvm Relevance vector machine regression object |
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. |
kernels | |
learningConverged | Whether or not the training converged |
learningResults | Struct with information about the convergence |
name | Relevance Vector Machine |
nameAbbreviation | RVM |
plotOptions | Plotting Options |
showProgressBar | |
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 |
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. | |
runRegressorOnGrid | ||
set | set the object properties | |
train | Train a prtAction object using training a prtDataSet object. |