代码搜索：Approximation

找到约 1,542 项符合「Approximation」的源代码

代码结果 1,542
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www.eeworm.com/read/397122/8065822

m trainlssvm.m

function [model,b,X,Y] = trainlssvm(model,X,Y) % Train the support values and the bias term of an LS-SVM for classification or function approximation % % >> [alpha, b] = trainlssvm({X,Y,type,gam,ke
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www.eeworm.com/read/331336/12832506

m trainlssvm.m

function [model,b,X,Y] = trainlssvm(model,X,Y) % Train the support values and the bias term of an LS-SVM for classification or function approximation % % >> [alpha, b] = trainlssvm({X,Y,type,gam,ke
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www.eeworm.com/read/143706/12849498

m evidence.m

function [net, gamma, logev] = evidence(net, x, t, num) %EVIDENCE Re-estimate hyperparameters using evidence approximation. % % Description % [NET] = EVIDENCE(NET, X, T) re-estimates the hyperparamete
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www.eeworm.com/read/140851/13058947

m evidence.m

function [net, gamma, logev] = evidence(net, x, t, num) %EVIDENCE Re-estimate hyperparameters using evidence approximation. % % Description % [NET] = EVIDENCE(NET, X, T) re-estimates the hyperpara
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www.eeworm.com/read/139320/13161376

m trainlssvm.m

function [model,b,X,Y] = trainlssvm(model,X,Y) % Train the support values and the bias term of an LS-SVM for classification or function approximation % % >> [alpha, b] = trainlssvm({X,Y,type,gam,ke
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www.eeworm.com/read/138798/13211948

m evidence.m

function [net, gamma, logev] = evidence(net, x, t, num) %EVIDENCE Re-estimate hyperparameters using evidence approximation. % % Description % [NET] = EVIDENCE(NET, X, T) re-estimates the hyperpara
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www.eeworm.com/read/324303/13273735

m trainlssvm.m

function [model,b,X,Y] = trainlssvm(model,X,Y) % Train the support values and the bias term of an LS-SVM for classification or function approximation % % >> [alpha, b] = trainlssvm({X,Y,type,gam,ke
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www.eeworm.com/read/318947/13465982

m trainlssvm.m

function [model,b,X,Y] = trainlssvm(model,X,Y) % Train the support values and the bias term of an LS-SVM for classification or function approximation % % >> [alpha, b] = trainlssvm({X,Y,type,gam,ke
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www.eeworm.com/read/316944/13514015

m trainlssvm.m

function [model,b,X,Y] = trainlssvm(model,X,Y) % Train the support values and the bias term of an LS-SVM for classification or function approximation % % >> [alpha, b] = trainlssvm({X,Y,type,gam,ke
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www.eeworm.com/read/304650/13790281

m fs01.m

function Xt_plus_1=fs01(Xt,w); %The local linear approximation method of the first order to predict a chaotic time series, after Farmer and Sidorowich,1987 %modified with help of constrained linea
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