This is a convenience interface that combines MLInput and MLOutput. Together these define a MLMethod that both accepts input and produces output. Input and output are defined as a simple array of double values. Many machine learning methods, such as neural networks and support vector machines handle input and output in this way, and thus implement this interface. Others, such as clustering, do not.
- All Known Implementing Classes:
- ART1, BasicNetwork, BasicPNN, BayesianNetwork, BoltzmannMachine, CPN, EncogProgram, FreeformNetwork, GaussianFitting, GenericEnsembleML, HopfieldNetwork, LinearRegression, NEATNetwork, NEATPopulation, PrgPopulation, RBFNetwork, SOM, SVM, ThermalNetwork
SCaVis 1.7 © jWork.org