代码搜索：kernel
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www.eeworm.com/read/203858/15350786
h memory.h
/*
* NOTE!!! memcpy(dest,src,n) assumes ds=es=normal data segment. This
* goes for all kernel functions (ds=es=kernel space, fs=local data,
* gs=null), as well as for all well-behaving user prog
www.eeworm.com/read/203482/15357929
m ilog.m
%ILOG Laplacian of Gaussian kernel
%
% L = ilog(w, sigma)
%
% Return a Laplacian of Gaussian (LOG) kernel of width (2w+1) with
% the specified sigma.
%
% SEE ALSO: zcross
%
% Copyright (c) Peter Corke
www.eeworm.com/read/203359/15360497
txt readme.txt
It's based on the usb-skeleton.c usb-to serial driver.
Using environment:
Redhat Linux 9.0 (Kernal 2.4.20-8)
Note: actually if you modified t
www.eeworm.com/read/203036/15367438
bat ms.bat
rem Batch file to make the Microsoft C version of the CTask kernel
rem and support routines.
make ctask.ms
make ctsup.ms
www.eeworm.com/read/202824/15372014
m ilog.m
%ILOG Laplacian of Gaussian kernel
%
% L = ilog(w, sigma)
%
% Return a Laplacian of Gaussian (LOG) kernel of width (2w+1) with
% the specified sigma.
%
% SEE ALSO: zcross
%
% Copyright (c) Peter Corke
www.eeworm.com/read/201748/15398046
m svmtest.m
clear
load trainresult;
load testing;
[N,M] = size(testset); % N is the size of test set
Ns = length(weight);
X = supportvectors;
for i=1:N
for j=1:Ns
switch kernel
www.eeworm.com/read/201748/15398058
asv svmtest.asv
clear
load trainresult;
load testing;
[N,M] = size(testset); % N is the size of test set
Ns = length(weight);
X = supportvectors;
for i=1:N
for j=1:Ns
switch kernel
www.eeworm.com/read/201218/15413209
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
www.eeworm.com/read/201179/15414824
readme
README
------
Note: any comments please goto wheelz at
This program is based on drcomsuite-0.1.12 and libdrcom-0.0-20050423-2
written by William Poetra Yoga Hadisoes
www.eeworm.com/read/200388/15434349
m isobel.m
%ISOBEL Sobel edge detector
%
% is = isobel(image)
% is = isobel(image, Dx)
% [ih,iv] = isobel(image)
% [ih,iv] = isobel(image, Dx)
%
% Applies the Sobel edge detector, which is the norm of the vertic