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©Marko Grönroos, 1998

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Säie: Hermoverkkopaketti SourceForgessa

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Newsgroups: sfnet.tiede.tekoaly
Subject: Hermoverkkopaketti SourceForgessa
From: magi AT iki PISTE fi (Marko Grönroos)
Date: 13 Nov 2000 01:47:58 +0200

Laittetaan nyt sitten tännekin, jos jotakuta kiinnostaa.

Inanna is a relatively small ANN library that I've been developing for
a few years for research purposes. It's very object-oriented design,
and written in C++ (g++) in Solaris and Linux. It's just gone through
heavy changes, and some restructuring is yet to be done, so the
current release should be considered as VERY ALPHA (developmental
release) VERSION!!!

            http://sourceforge.net/projects/inanna/

The OO design is somewhat open and generic, which has been my main
design goal. Some main features:

         * Object-oriented network structure (network, neurons, connections)
         * Pattern set objects
         * Pattern set file format objects
         * Equalization and unequalization (!) objects
         * Training algorithm objects
         * Early stopping objects
         * Network visualization objects (partially implemented)
         * Some other stuff
         * Some example projects

The architecture is such that user-defined components should be very
easy to implement.

Users and especially developers are welcome. The library is LGPL
licensed, and CVS accounts are available at SourceForge.net.

It uses the GNU "./configure ; make ; make install" build system,
although you probably won't get it that easy... If you try it, PLEASE
tell me if you could get it compiled or even working, and what
problems you had (I KNOW you'll have many). I really don't know how
easy it is to use...

It has some nice documentation written with LyX, but it's not at
SourceForge quite yet.

History: I developed the previous version for a study involving
evolutionary design of neural networks. That part of the project is
currently broken though, because of various library
incompatibilities. But, maybe it will work again some day. Previously
Inanna used SNNS for training, but now it implements two training
algorithms: Backprop and Resilient Backprop. Not quite as fast as
SNNS, but almost (I'm working on that).

--
-- Marko Grönroos, magi AT iki PISTE fi (http://www.iki.fi/magi/)
-- Paradoxes are the source of truth and the end of wisdom

Edellinen säie: Luovuus
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