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Kohonen's Self Organizing Maps in C++ with Application in Computer Vision Area

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20 Nov 2007GPL318 min read 173.6K   7.1K   109  
The article demonstrates the self organizing maps clustering approach for unsupervised AI classification tasks with application examples in computer vision area for faces clustering and recognition
========================================================================
    CONSOLE APPLICATION : som Project Overview
========================================================================

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


som.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.

som.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 som.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)


Written By
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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