代码搜索：preprocessing
找到约 856 项符合「preprocessing」的源代码
代码结果 856
www.eeworm.com/read/474600/6813572
m competitive_learning.m
function [patterns, targets, label, W] = Competitive_learning(train_patterns, train_targets, params, plot_on)
% Perform preprocessing using a competitive learning network
% Inputs:
% patterns -
www.eeworm.com/read/195441/8155612
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/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/168845/5431365
hpp eti.hpp
#ifndef BOOST_MPL_AUX_CONFIG_ETI_HPP_INCLUDED
#define BOOST_MPL_AUX_CONFIG_ETI_HPP_INCLUDED
// Copyright Aleksey Gurtovoy 2001-2004
//
// Distributed under the Boost Software License, Version
www.eeworm.com/read/191902/8417409
m competitive_learning.m
function [features, targets, label, W] = Competitive_learning(train_features, train_targets, params, region, plot_on)
% Perform preprocessing using a competitive learning network
% Inputs:
% fea
www.eeworm.com/read/177129/9469033
m competitive_learning.m
function [features, targets, label, W] = Competitive_learning(train_features, train_targets, params, region, plot_on)
% Perform preprocessing using a competitive learning network
% Inputs:
% fea
www.eeworm.com/read/349842/10796978
m competitive_learning.m
function [features, targets, label, W] = Competitive_learning(train_features, train_targets, params, region, plot_on)
% Perform preprocessing using a competitive learning network
% Inputs:
% fea
www.eeworm.com/read/447044/7559958
xml_activatingguiforseries60only listing12-02_build.xml_activatingguiforseries60only
www.eeworm.com/read/447044/7559976
xml_definingvariablesandsymbols listing8-15_build.xml_definingvariablesandsymbols
www.eeworm.com/read/146295/12660894
m competitive_learning.m
function [features, targets, label, W] = Competitive_learning(train_features, train_targets, params, region, plot_on)
% Perform preprocessing using a competitive learning network
% Inputs:
% fea