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/190459/8443075

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/430320/8756376

m mls1d.m

% ONE-DIMENSIONAL MLS APPROXIMATION clear all % PROBLEM DIFINITION l = 10.0; dx = 0.5; % SET UP NODAL COORDINATES xi = [0.0 : dx : l]; nnodes = length(xi); % SET UP COORDINATES OF EV
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www.eeworm.com/read/430320/8756388

m mls1d3.m

% ONE-DIMENSIONAL MLS APPROXIMATION clear all % PROBLEM DIFINITION l = 10.0; dx = 0.5; % SET UP NODAL COORDINATES xi = [0.0 : dx : l]; nnodes = length(xi); % SET UP COORDINATES OF EV
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www.eeworm.com/read/429504/8804807

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/428451/8867232

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/427586/8932016

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/185152/9054801

m apprgrdn.m

function g = apprgrdn(x,f,fun,deltax,obj) % Usage: % g = apprgrdn(x,f,fun,deltax,obj) % Function apprgrdn.m performs the finite difference approximation % of the gradient at a point . %
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www.eeworm.com/read/183445/9158691

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/374698/9388868

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/177674/9442386

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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