代码搜索：Gradient
找到约 2,951 项符合「Gradient」的源代码
代码结果 2,951
www.eeworm.com/read/160391/5571177
m mixexp_graddesc.m
%%%%%%%%%%
function [theta, eta] = mixture_of_experts(q, data, num_iter, theta, eta)
% MIXTURE_OF_EXPERTS Fit a piecewise linear regression model using stochastic gradient descent.
% [theta, et
www.eeworm.com/read/160391/5571423
m maximize_params.m
function CPD = maximize_params(CPD, temp)
% MAXIMIZE_PARAMS Find ML params of an MLP using Scaled Conjugated Gradient (SCG)
% CPD = maximize_params(CPD, temperature)
% temperature parameter is igno
www.eeworm.com/read/474583/6812914
m grad.m
function [fx,fy,fz] = grad(M, options)
% grad - gradient, forward differences
%
% [gx,gy] = grad(M, options);
% or
% g = grad(M, options);
%
% options.bound = 'per' or 'sym'
% options.order =
www.eeworm.com/read/474583/6813006
svn-base grad.m.svn-base
function [fx,fy,fz] = grad(M, options)
% grad - gradient, forward differences
%
% [gx,gy] = grad(M, options);
% or
% g = grad(M, options);
%
% options.bound = 'per' or 'sym'
% options.order =
www.eeworm.com/read/295595/8150723
m gradwfixed.m
function [grad] = gradwfixed(Sigma,indsup,Alpsup,C,Xapp,yapp,Sigmaold,pow);
%GRADWFIXED Computes the gradient of an upper bound on SVM loss wrt SIGMA^POW
% GRAD = GRADWFIXED(SIGMA,INDSUP,ALPSUP,C,X
www.eeworm.com/read/394381/8227675
m definev.m
function [v,dv]= definev(g,x,l,u);
%DEFINEV Scaling vector and derivative
%
% [v,dv]= DEFINEV(g,x,l,u) returns v, distances to the
% bounds corresponding to the sign of the gradient g, where
%
www.eeworm.com/read/393865/8257742
m gradwfixed.m
function [grad] = gradwfixed(Sigma,indsup,Alpsup,C,Xapp,yapp,Sigmaold,pow);
%GRADWFIXED Computes the gradient of an upper bound on SVM loss wrt SIGMA^POW
% GRAD = GRADWFIXED(SIGMA,INDSUP,ALPSUP,C,X
www.eeworm.com/read/170936/9779280
m olgd.m
function [net, options, errlog, pointlog] = olgd(net, options, x, t)
%OLGD On-line gradient descent optimization.
%
% Description
% [NET, OPTIONS, ERRLOG, POINTLOG] = OLGD(NET, OPTIONS, X, T) uses on
www.eeworm.com/read/170936/9779310
m gpgrad.m
function g = gpgrad(net, x, t)
%GPGRAD Evaluate error gradient for Gaussian Process.
%
% Description
% G = GPGRAD(NET, X, T) takes a Gaussian Process data structure NET
% together with a matrix X of
www.eeworm.com/read/415313/11076540
m olgd.m
function [net, options, errlog, pointlog] = olgd(net, options, x, t)
%OLGD On-line gradient descent optimization.
%
% Description
% [NET, OPTIONS, ERRLOG, POINTLOG] = OLGD(NET, OPTIONS, X, T) uses on