C# 클래스 Encog.Neural.Networks.SVM.SVMNetwork

This is a network that is backed by one or more Support Vector Machines (SVM). It is designed to function very similarly to an Encog neural network, and is largely interchangeable with an Encog neural network. The support vector machine supports several types. Regression is used when you want the network to predict a value, given the input. Function approximation is a good example of regression. Classification is used when you want the SVM to group the input data into one or more classes. Support Vector Machines typically have a single output. Neural networks can have multiple output neurons. To get around this issue, this class will create multiple SVM's if there is more than one output specified. Because a SVM is trained quite differently from a neural network, none of the neural network training classes will work. This class must be trained using SVMTrain.
상속: BasicNetwork
파일 보기 프로젝트 열기: encog/encog-silverlight-core 1 사용 예제들

공개 메소드들

메소드 설명
Compute ( INeuralData input ) : INeuralData

Compute the output for the given input.

Compute ( INeuralData input, NeuralOutputHolder useHolder ) : INeuralData

Compute the output for the given input.

CreatePersistor ( ) : IPersistor

Create a persistor for this object.

MakeSparse ( INeuralData data ) : Encog.MathUtil.LIBSVM.svm_node[]

Convert regular Encog NeuralData into the "sparse" data needed by an SVM.

SVMNetwork ( int inputCount, int outputCount, SVMType svmType, KernelType kernelType ) : System

Construct a SVM network.

SVMNetwork ( int inputCount, int outputCount, bool regression ) : System

Construct an SVM network. For regression it will use an epsilon support vector. Both types will use an RBF kernel.

메소드 상세

Compute() 공개 메소드

Compute the output for the given input.
public Compute ( INeuralData input ) : INeuralData
input INeuralData The input to the SVM.
리턴 INeuralData

Compute() 공개 메소드

Compute the output for the given input.
public Compute ( INeuralData input, NeuralOutputHolder useHolder ) : INeuralData
input INeuralData The input to the SVM.
useHolder NeuralOutputHolder The output holder to use.
리턴 INeuralData

CreatePersistor() 공개 메소드

Create a persistor for this object.
public CreatePersistor ( ) : IPersistor
리턴 IPersistor

MakeSparse() 공개 메소드

Convert regular Encog NeuralData into the "sparse" data needed by an SVM.
public MakeSparse ( INeuralData data ) : Encog.MathUtil.LIBSVM.svm_node[]
data INeuralData The data to convert.
리턴 Encog.MathUtil.LIBSVM.svm_node[]

SVMNetwork() 공개 메소드

Construct a SVM network.
public SVMNetwork ( int inputCount, int outputCount, SVMType svmType, KernelType kernelType ) : System
inputCount int The input count.
outputCount int The output count.
svmType SVMType The type of SVM.
kernelType KernelType The SVM kernal type.
리턴 System

SVMNetwork() 공개 메소드

Construct an SVM network. For regression it will use an epsilon support vector. Both types will use an RBF kernel.
public SVMNetwork ( int inputCount, int outputCount, bool regression ) : System
inputCount int The input count.
outputCount int The output count.
regression bool True if this network is used for regression.
리턴 System