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/428269/8880222
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/177674/9442505
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/176823/9483194
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/372113/9521288
m cart.m
function test_targets = CART(train_patterns, train_targets, test_patterns, params)
% Classify using classification and regression trees
% Inputs:
% training_patterns - Train patterns
% traini
www.eeworm.com/read/362306/10006177
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/362008/10023965
m cart.m
function test_targets = CART(train_patterns, train_targets, test_patterns, params)
% Classify using classification and regression trees
% Inputs:
% training_patterns - Train patterns
% traini
www.eeworm.com/read/357874/10199160
m cart.m
function test_targets = CART(train_patterns, train_targets, test_patterns, params)
% Classify using classification and regression trees
% Inputs:
% training_patterns - Train patterns
% traini
www.eeworm.com/read/161855/10361049
1 mailinspect.1
\" t
.TH MAILINSPECT 1 "Bayesian Text Classification Tools" "Version 1.3" ""
.SH NAME
mailinspect \- sort an mbox by category and pipe emails to a command.
.SH SYNOPSIS
.HP
.B mailinspect [-zjiI]
-c
www.eeworm.com/read/418756/10928173
m adademo.m
function MOV=adademo
% ADADEMO AdaBoost demo
% ADADEMO runs AdaBoost on a simple two dimensional classification
% problem.
% Written by Andrea Vedaldi - 2006
% http://vision.ucla.edu/~vedaldi
do_
www.eeworm.com/read/458392/7297151
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