代码搜索:preprocessing

找到约 856 项符合「preprocessing」的源代码

代码结果 856
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txt filter.txt

This document explains the concept of "filtering" in UPX. Basically filtering is a data preprocessing method which could improve the compression ratio of the files UPX processes. Currently the filter
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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 1.0.
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m classifier_commands.m

function classifier_commands(command) %This function processes events from the main (single-algorithm) GUI screen persistent Preprocessing_methods; persistent Algorithms; if (isempty(Preproces
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m postlssvm.m

function [model,Yt] = postlssvm(model,Xt,Yt) % Postprocessing of the LS-SVM % % These functions should only be called by trainlssvm or by % simlssvm. At first the preprocessing assigns a label to each
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m classifier_commands.m

function classifier_commands(command) %This function processes events from the main (single-algorithm) GUI screen persistent Preprocessing_methods; persistent Algorithms; if (isempty(Preproces
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m postlssvm.m

function [model,Yt] = postlssvm(model,Xt,Yt) % Postprocessing of the LS-SVM % % These functions should only be called by trainlssvm or by % simlssvm. At first the preprocessing assigns a label to each
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m postlssvm.m

function [model,Yt] = postlssvm(model,Xt,Yt) % Postprocessing of the LS-SVM % % These functions should only be called by trainlssvm or by % simlssvm. At first the preprocessing assigns a label to each
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m postlssvm.m

function [model,Yt] = postlssvm(model,Xt,Yt) % Postprocessing of the LS-SVM % % These functions should only be called by trainlssvm or by % simlssvm. At first the preprocessing assigns a label to each
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m postlssvm.m

function [model,Yt] = postlssvm(model,Xt,Yt) % Postprocessing of the LS-SVM % % These functions should only be called by trainlssvm or by % simlssvm. At first the preprocessing assigns a label to each
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m postlssvm.m

function [model,Yt] = postlssvm(model,Xt,Yt) % Postprocessing of the LS-SVM % % These functions should only be called by trainlssvm or by % simlssvm. At first the preprocessing assigns a label to each