| MATLAB File Help: prtClassMatlabNnet |
prtClassMatlabNnet Support vector machine classifier using the MATLAB neural network toolbox (requires NNET toolbox) CLASSIFIER = prtClassMatlabNnet returns a neural network classifier using the MATLAB NNET toolbox (additonal product, not included) A prtClassMatlabNnet object inherits all properties from the abstract class prtClass. In addition is has the following properties; complete documentation for these properties can be found in the help for the newpr.m function in the MATLAB NNET toolbox. Si, TFi, BTF, BLF, PF, IPF, OPF, DDF % Example usage: TestDataSet = prtDataGenBimodal; % Create some test and TrainingDataSet = prtDataGenBimodal; % training data classifier = prtClassMatlabNnet; % Create a classifier classifier = classifier.train(TrainingDataSet); % Train classified = run(classifier, TestDataSet); % Test 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');
| Superclasses | prtClass |
| Sealed | false |
| Construct on load | false |
| prtClassMatlabNnet | Support vector machine classifier using the MATLAB neural network toolbox (requires NNET toolbox) |
| BLF | See help for newpr |
| BTF | See help for newpr |
| DDF | See help for newpr |
| IPF | See help for newpr |
| OPF | See help for newpr |
| PF | See help for newpr |
| Si | Number of layers in hidden element |
| TFi | See help for newpr |
| 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 | True |
| isSupervised | True |
| isTrained | Indicates if prtAction object has been trained. |
| name | MATLAB Neural Network |
| nameAbbreviation | MLNN |
| nnet | The base neural network |
| showProgressBar | |
| 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 |
| verboseStorage | Specifies whether or not to store the training prtDataset. |
| 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 output confidence of a prtClass 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. |