MultidimensionalRegression.h File Reference

This class implements the Multidimensional Regression meta algorithm. Multidimensional Regressionacts as a meta-algorithm for regression that allows several one-dimensional regression algorithms (such as Linear Regression), to be combined together to allow an M-dimensional signal to be mapped to an N-dimensional signal. This works by training N seperate regression algorithms (one for each dimension), each with an M-dimensional input. More...

#include "../../CoreModules/Regressifier.h"
#include "../LinearRegression/LinearRegression.h"

Go to the source code of this file.

Classes
class	MultidimensionalRegression

Detailed Description

This class implements the Multidimensional Regression meta algorithm. Multidimensional Regressionacts as a meta-algorithm for regression that allows several one-dimensional regression algorithms (such as Linear Regression), to be combined together to allow an M-dimensional signal to be mapped to an N-dimensional signal. This works by training N seperate regression algorithms (one for each dimension), each with an M-dimensional input.

Author

Nicholas Gillian ngill.nosp@m.ian@.nosp@m.media.nosp@m..mit.nosp@m..edu

Version

1.0

In addition to enabling one-dimensional regression algorithms (such as Linear Regression) to be used for mapping N-dimensional output signals, Multidimensional Regression can also be useful for multi-dimensional regression algorithms (such as Multi Layer Perceptrons), as it enables you to train N seperate MLP algorithms (one for each output signal), which might provide better mapping results than trying to train one MLP algorithm that can successfully map all N-dimensions at once.

Definition in file MultidimensionalRegression.h.