代码搜索：gradient

找到约 2,951 项符合「gradient」的源代码

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m gradlbfixed.m

function [grad] = gradlbfixed(Sigma,indsup,Alpsup,w0,C,Xapp,yapp,pow); %GRADLBFIXED Computes the gradient of an upper bound on SVM loss wrt SIGMA^POW % GRAD = GRADLBFIXED(SIGMA,INDSUP,ALPSUP,W0,C,X
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m gradlfixed.m

function [grad] = gradlfixed(Sigma,indsup,Alpsup,C,Xapp,yapp,pow); %GRADLFIXED Computes the gradient of an upper bound on SVM loss wrt SIGMA^POW % GRAD = GRADLFIXED(SIGMA,INDSUP,ALPSUP,C,XAPP,YAPP,
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www.eeworm.com/read/386504/8739631

h hough.h

/********************************************************************* * This file is part of the PRAPI library. * * Copyright (C) 2001-2002 Topi M鋏np滗 * Copyright (C) 2001 Jaakko Viertola * All
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htm gbayes.htm

Netlab Reference Manual gbayes gbayes Purpose Evaluate gradient of Bayesian error function for network. Synopsis
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htm gradchek.htm

Netlab Reference Manual gradchek gradchek Purpose Checks a user-defined gradient function using finite differences. Synopsi
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htm netgrad.htm

Netlab Reference Manual netgrad netgrad Purpose Evaluate network error gradient for generic optimizers Synopsis
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m gazbgradeval.m

function [nsol, val] = gaZBGradEval(sol,options) % This evaluation function takes in a potential solution and two options % options(3) is the percent of time to perform the gradient heuristic to the
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www.eeworm.com/read/428269/8880182

m gradlbfixed.m

function [grad] = gradlbfixed(Sigma,indsup,Alpsup,w0,C,Xapp,yapp,pow); %GRADLBFIXED Computes the gradient of an upper bound on SVM loss wrt SIGMA^POW % GRAD = GRADLBFIXED(SIGMA,INDSUP,ALPSUP,W0,C,X
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www.eeworm.com/read/428269/8880190

m gradlfixed.m

function [grad] = gradlfixed(Sigma,indsup,Alpsup,C,Xapp,yapp,pow); %GRADLFIXED Computes the gradient of an upper bound on SVM loss wrt SIGMA^POW % GRAD = GRADLFIXED(SIGMA,INDSUP,ALPSUP,C,XAPP,YAPP,
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www.eeworm.com/read/427260/8955567

m gazbgradeval.m

function [nsol, val] = gaZBGradEval(sol,options) % This evaluation function takes in a potential solution and two options % options(3) is the percent of time to perform the gradient heuristic to the
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