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/393865/8257755
m exlarsignalclassif.m
% Example of wavelet discriminant basis signal classification
%
%
% 20/12/2005
clear all
close all
nbtrain=100;
noise=1;
nf=128;
localisation=100:105;
saut=1;
name={'HeaviSine' 'D
www.eeworm.com/read/170936/9779255
m demev2.m
%DEMEV2 Demonstrate Bayesian classification for the MLP.
%
% Description
% A synthetic two class two-dimensional dataset X is sampled from a
% mixture of four Gaussians. Each class is associated wit
www.eeworm.com/read/415313/11076510
m demev2.m
%DEMEV2 Demonstrate Bayesian classification for the MLP.
%
% Description
% A synthetic two class two-dimensional dataset X is sampled from a
% mixture of four Gaussians. Each class is associated wit
www.eeworm.com/read/413912/11137216
m demev2.m
%DEMEV2 Demonstrate Bayesian classification for the MLP.
%
% Description
% A synthetic two class two-dimensional dataset X is sampled from a
% mixture of four Gaussians. Each class is associated wit
www.eeworm.com/read/266128/11238913
txt fknn.txt
function [predicted,memberships, numhits] = fknn(data, labels, test, ...
testlabels, k_values, info, fuzzy)
% FKNN Fuzzy k-nearest neighbor classification algorithm.
% Y = FKNN(DATA, LABELS,
www.eeworm.com/read/113670/15451514
cla irisrul.cla
classification
8 4
0
w trapezoid 4.300000 4.300000 4.330000 5.830000
x trapezoid 2.300000 2.300000 2.342000 4.442000
y trapezoid 1.000000 1.000000 1.018000 1.918000
z trapezoid 0.100000 0.100000 0.110
www.eeworm.com/read/107565/15604907
txt readmeraf2.txt
RAFISHER2CDA Canonical Discriminant Analysis. While RAFisher1 is a procedure
that produces very different functions for classification that are also called
linear discriminant analysis, RAFisher2cda
www.eeworm.com/read/192513/8378030
m svcm_run.m
function [ypred,margin] = svcm_run(xrun,xtrain,ytrain,atrain,btrain);
% function [ypred,margin] = svcm_run(xrun,xtrain,ytrain,atrain,btrain);
%
% support vector classification machine
% soft margin
%
www.eeworm.com/read/190459/8443075
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/289680/8535012
m fwd.m
function y = fwd(net,x)
% FWD
%
% Compute the output of a multi-class support vector classification network.
%
% y = fwd(net, x);
%
% where x is a matrix of input patterns, where each colu