| 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. |