代码搜索：kernel

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www.eeworm.com/read/151396/12216265

bas zip.bas

Attribute VB_Name = "ZipUtils" '============================================================================== 'Richsoft Computing 2001 'Richard Southey 'This code is e-mailware, if you use it ple
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www.eeworm.com/read/151211/12227329

txt 67.txt

如何使程序不在Ctrl+Alt+Del弹出的对话框内？ 声明： Declare Function RegisterServiceProcess Lib "kernel32" (ByVal _ ProcessID As Long, ByVal ServiceFlags As Long) As Long Declare Function GetCurrentProcess
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www.eeworm.com/read/150905/12248381

m svc.m

%SVC Support Vector Classifier % % [W,J] = SVC(A,TYPE,PAR,C) % % INPUT % A Dataset % TYPE Type of the kernel (optional; default: 'p') % PAR Kernel parameter (optional; default: 1) % C
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www.eeworm.com/read/150761/12264596

m c_svcdemo.m

% ------- OSU C-SVM CLASSIFIER TOOLBOX Demonstrations--- % % 1) Construct a linear SVM Classifier and test it % 2) Construct a nonlinear SVM Classifier (polynomial kernel) and t
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www.eeworm.com/read/150761/12264616

m u_svcdemo.m

% ------- OSU nu-SVM CLASSIFIER TOOLBOX Demonstrations--- % % 1) Construct a linear SVM Classifier and test it % 2) Construct a nonlinear SVM Classifier (polynomial kernel) and
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www.eeworm.com/read/150760/12266176

m rsde.m

function model = rsde(X,options) % RSDE Reduced Set Density Estimator. % % Synopsis: % model = rsde(X,options) % % Description: % This function implements the Reduced Set Density Estimator % [Giro
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www.eeworm.com/read/150760/12266179

m~ rsde.m~

function model = rsde(X,options) % RSDE Reduced Set Density Estimator. % % Synopsis: % model = rsde(X,options) % % Description: % This function implements the Reduced Set Density Estimator % [Giro
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www.eeworm.com/read/150749/12267206

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/150749/12267339

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/150749/12267348

m afe.m

function [features,eigvec,eigvals] = AFE(Xs,kernel, kernel_pars,X,type,nb,eigvec,eigvals) % Automatic Feature Extraction by Nystr鰉 method % % % >> features = AFE(X, kernel, sig2, Xt) % % Description %
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