代码搜索：kernel

找到约 10,000 项符合「kernel」的源代码

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www.eeworm.com/read/130383/14196273

m svmroc.m

function [xsup,w,w0,pos,timeps,alpha,matriceind]=svmroc(x,y,C,kppv,margin,lambda,kernel,kerneloption,verbose,span,qpsize) % USAGE % % [xsup,w,w0,pos,timeps,alpha,matriceind]=svmroc(x,y,C,kppv,margin
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www.eeworm.com/read/130383/14196306

m svmclassl2.m

function [xsup,w,d,pos,timeps,alpha,obj]=svmclass(x,y,c,lambda,kernel,kerneloption,verbose,span,alphainit) % USAGE [xsup,w,b,pos,timeps,alpha]=svmclass(x,y,c,lambda,kernel,kerneloption,verbose,span) %
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www.eeworm.com/read/130070/14209381

bas module1.bas

Attribute VB_Name = "Module1" Option Explicit Public Const GENERIC_WRITE = &H40000000 Public Const GENERIC_READ = &H80000000 Public Const OPEN_EXISTING = 3 Public Const FILE_FLAG_OVERLAPPED
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www.eeworm.com/read/128468/14295434

m kernelpca.m

function [Z,Lambda]=kernelpca(X,T,l,ker,arg,display) % KERNELPCA computes kernel Principal Component Analysis. % [Z,lambda]=kernelpca(X,T,l,ker,arg,display) % % KERNELPCA computes Principal Componen
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www.eeworm.com/read/128468/14295493

m ka.m

function [Alpha, bias, margin, t, flps] = ka(X,I,ker,arg,C,tmax,epsilon,Ni,mi) % KA kernel-Adatron algorithm solving SVM (L1) problem. % [Alpha,bias,margin,t,flps]=ka(X,I,ker,arg,C,tmax,epsilon,Ni,mi)
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www.eeworm.com/read/128238/14309920

bas myini.bas

Attribute VB_Name = "ini" Declare Function WritePrivateProfileString _ Lib "kernel32" Alias "WritePrivateProfileStringA" _ (ByVal lpApplicationname As String, ByVal _ lpKeyName As Any, ByVal lsStr
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www.eeworm.com/read/128193/14311426

m getkernel.m

function kernel = getkernel(net) % GETKERNEL % % Accessor method returning the kernel used in a support vector classification % network. % % ker = getkernel(net) % % File : @svc/
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www.eeworm.com/read/128193/14311437

m display.m

function display(ker) % DISPLAY % % Display a textual representation of a radial basis kernel object. % % display(ker); % % File : @rbf/display.m % % Date : Tuesday 12th
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www.eeworm.com/read/128193/14311493

m train.m

function net = train(tutor, x, y, C, kernel, zeta, net) % TRAIN % % Train a support vector classification network, using the sequential minimal % optimisation algorithm. % % net = train(tut
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www.eeworm.com/read/128193/14311506

m display.m

function display(ker) % DISPLAY % % Display a textual representation of a polynomial kernel object. % % display(ker); % % File : @polynomial/display.m % % Date : Tuesday
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