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/491824/6426887
m nnd10lc.m
function nnd10lc(cmd,arg1,arg2,arg3)
% NND10LC Linear pattern classification demonstration.
% Copyright 1994-2002 PWS Publishing Company and The MathWorks, Inc.
% $Revision: 1.7 $
% First Versio
www.eeworm.com/read/259886/11759850
m nnd10lc.m
function nnd10lc(cmd,arg1,arg2,arg3)
% NND10LC Linear pattern classification demonstration.
% Copyright 1994-2002 PWS Publishing Company and The MathWorks, Inc.
% $Revision: 1.7 $
% First Versio
www.eeworm.com/read/414357/11119178
m nnd10lc.m
function nnd10lc(cmd,arg1,arg2,arg3)
% NND10LC Linear pattern classification demonstration.
% Copyright 1994-2002 PWS Publishing Company and The MathWorks, Inc.
% $Revision: 1.7 $
% First Versio
www.eeworm.com/read/175317/9552356
m plot2d.m
function plot_data(X,Y,markersize)
% plot2D(X,Y)
% plots a binary classification dataset of 2 dimensions
pos=find(Y==1);
neg=find(Y==-1);
unlab=find(Y==0);
%if ~isempty(unlab)
plot(X(unlab,1),X(un
www.eeworm.com/read/398934/7908806
m fenleiqi1.m
function y=fenleiqi(traindata,tdata)
[n N]=size(traindata);
[a1 a2]=Classification(traindata(:,N));%找到类变量分类的范围
[traindata A B]=guiyihua(traindata);
H1=[];H2=[];H3=[];
for i=1:n
if traindata(
www.eeworm.com/read/271350/4229281
cc mlp.cc
const char *help = "\
progname: mlp.cc\n\
code2html: This program trains a MLP for 2 class classification.\n\
version: Torch3 vision2.1, 2003-2006\n\
(c) Sebastien Marcel (marcel@idiap.ch)\n";
/** To
www.eeworm.com/read/429878/8784035
htm demglm1.htm
Netlab Reference Manual demglm1
demglm1
Purpose
Demonstrate simple classification using a generalized linear model.
Synopsi
www.eeworm.com/read/429878/8784096
htm demmlp2.htm
Netlab Reference Manual demmlp2
demmlp2
Purpose
Demonstrate simple classification using a multi-layer perceptron
Synopsis
www.eeworm.com/read/427909/8913053
m logist2fit.m
function [beta, p] = logist2Fit(y, x, addOne, w)
% LOGIST2FIT 2 class logsitic classification
% function beta = logist2Fit(y,x, addOne)
%
% y(i) = 0/1
% x(:,i) = i'th input - we optionally append
www.eeworm.com/read/373249/9467866
m logist2fit.m
function [beta, p] = logist2Fit(y, x, addOne, w)
% LOGIST2FIT 2 class logsitic classification
% function beta = logist2Fit(y,x, addOne)
%
% y(i) = 0/1
% x(:,i) = i'th input - we optionally append