代码搜索：classification
找到约 3,679 项符合「classification」的源代码
代码结果 3,679
www.eeworm.com/read/413492/11154012
m main_near_neighbor_fucntion_criteria1.m
%***近邻函数值准则 classification algorithm simulation ***%
% Author: Feng Shuo
% Student ID: 1030520508
% Date 2007.04.19
clc;
clear;
%% sample,point coordinate
sample= [2 1;
1 2;
www.eeworm.com/read/413492/11154018
m main_near_neighbor_fucntion_criteria.m
%***近邻函数值准则 classification algorithm simulation ***%
% Author: Feng Shuo
% Student ID: 1030520508
% Date 2007.04.19
clc;
clear;
%% sample,point coordinate
sample= [2 1;
1 2;
www.eeworm.com/read/413492/11154019
asv main_near_neighbor_fucntion_criteria.asv
%***近邻函数值准则 classification algorithm simulation ***%
% Author: Feng Shuo
% Student ID: 1030520508
% Date 2007.04.19
clc;
clear;
%% sample,point coordinate
sample= [2 1;
1 2;
www.eeworm.com/read/148342/12474626
m svcinfo.m
function svcinfo(trn,tst,ker,alpha,bias)
%SVCINFO Support Vector Classification Results
%
% Usage: svcinfo(trn,tst,ker,alpha,bias)
%
% Parameters: trn - Training set
% tst - Test
www.eeworm.com/read/334876/12565490
m char3.m
%% Character Recognition Example (III):Training a Simple NN for
%% classification
%% Read the image
I = imread('sample.bmp');
%% Image Preprocessing
img = edu_imgpreprocess(I);
for cnt = 1:5
www.eeworm.com/read/146640/12628623
m svcinfo.m
function svcinfo(trn,tst,ker,alpha,bias)
%SVCINFO Support Vector Classification Results
%
% Usage: svcinfo(trn,tst,ker,alpha,bias)
%
% Parameters: trn - Training set
% tst - Test
www.eeworm.com/read/112466/15484756
tex biblio.tex
\rhead{\em{Bibliography}}
\begin{thebibliography} {99}
\bibitem{kn:thesis} K. F. Lai, ``Deformable Contours: Modeling, Extraction, Detection and Classification,''{\em Phd Thesis, Electrical Engine
www.eeworm.com/read/111603/15509361
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
www.eeworm.com/read/111603/15509382
m dagsvm.m
function net = dagsvm(arg)
% PAIRWISE
%
% Construct a dag-svm multi-class support vector classification network.
%
% Examples:
%
% % default constructor (a 0-class dagsvm network!)
%
%
www.eeworm.com/read/290976/8446244
m pdfbclassify_texture.m
% pdfbclassify_texture.m
% written by: Duncan Po
% Date: December 3, 2002
% perform texture classification based on contourlets
%
% Usage: kld = pdfbclassify_texture(qimage, qformat, tdb, tdir, m