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Backpropagation Artificial Neural Network in C++

, 20 May 2008 GPL3
This article demonstrates a backpropagation artificial neural network console application with validation and test sets for performance estimation using uneven distribution metrics.
bin
ann1Dn.exe
dat
red.dat
red.hea
iris.nn
setosa_versi.dat
virgi.dat
void
src
Lib
LibNN
========================================================================
    CONSOLE APPLICATION : ann1Dn Project Overview
========================================================================

AppWizard has created this ann1Dn application for you.  
This file contains a summary of what you will find in each of the files that
make up your ann1Dn application.


ann1Dn.vcproj
    This is the main project file for VC++ projects generated using an Application Wizard. 
    It contains information about the version of Visual C++ that generated the file, and 
    information about the platforms, configurations, and project features selected with the
    Application Wizard.

ann1Dn.cpp
    This is the main application source file.

/////////////////////////////////////////////////////////////////////////////
Other standard files:

StdAfx.h, StdAfx.cpp
    These files are used to build a precompiled header (PCH) file
    named ann1Dn.pch and a precompiled types file named StdAfx.obj.

/////////////////////////////////////////////////////////////////////////////
Other notes:

AppWizard uses "TODO:" comments to indicate parts of the source code you
should add to or customize.

/////////////////////////////////////////////////////////////////////////////

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License

This article, along with any associated source code and files, is licensed under The GNU General Public License (GPLv3)

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About the Author

Chesnokov Yuriy
Engineer
Russian Federation Russian Federation
Highly skilled Engineer with 14 years of experience in academia, R&D and commercial product development supporting full software life-cycle from idea to implementation and further support. During my academic career I was able to succeed in MIT Computers in Cardiology 2006 international challenge, as a R&D and SW engineer gain CodeProject MVP, find algorithmic solutions to quickly resolve tough customer problems to pass product requirements in tight deadlines. My key areas of expertise involve Object-Oriented
Analysis and Design OOAD, OOP, machine learning, natural language processing, face recognition, computer vision and image processing, wavelet analysis, digital signal processing in cardiology.

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