FANN accepted to participate in Google Summer of Code

[Steffen Nissen]

The FANN library has been accepted to participate in Google Summer of Code.

The library is not on the GSoC page (http://code.google.com/soc/) yet, but
it should be there soon.

So now its up to the students to apply for GSoC.

[Josh Menke]

Maybe I'm coming in a little late for feature suggestions, but in light of
what is "hot" in the academic world now I would suggest adding this:

1. First, add a softmax objective function fo the ANN. This makes the
outputs of multi-class problems equal to the original output over the sum of
the rest of the outputs. It's also the canonical link function for a
multi-nomial output.

2. Then, add the Bayesian neural network training originally proposed by
MacKay and explained well in Bishop's latest book, "Pattern Recognition and
Machine Learning"

http://research.microsoft.com/~cmbishop/PRML/index.htm

Yes, the ability to train ANNs in a Bayesian fashion is not new, but it's
also not part of any industry strength package yet.

This would allow you to train without using a hold-out set, and would
automatically handle weight decay and its corresponding parameter.

3. Add support for modeling time-varying (non-stationary) weights. Have the
weights be time dependent. One way to do this is through an extended Kalman
Filter. The following paper shows how to do this for just a single node ANN:

http://citeseer.ist.psu.edu/penny99dynamic.html

You could extend that, and you could use Bishop's book to get the
derivatives you need to do that.


Just my suggestions!

--Josh

Post by Steffen Nissen
The FANN library has been accepted to participate in Google Summer of Code.
The library is not on the GSoC page (http://code.google.com/soc/) yet, but
it should be there soon.
So now its up to the students to apply for GSoC.
--
Steffen Nissen - http://MySpace.com/SteffenNissen
Project Administrator - Fast Artificial Neural Network Library (fann)
http://fann.sf.net
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[Steffen Nissen]

Seems like reasonable suggestions, could you please submit them to this
thread (http://leenissen.dk/fann/forum/viewtopic.php?p=33) on the forum, I
will then make sure that the fann GSoC page says that more ideas are
available there.

Best Regards,
Steffen

Post by Josh Menke
Maybe I'm coming in a little late for feature suggestions, but in light of
1. First, add a softmax objective function fo the ANN. This makes the
outputs of multi-class problems equal to the original output over the sum of
the rest of the outputs. It's also the canonical link function for a
multi-nomial output.
2. Then, add the Bayesian neural network training originally proposed by
MacKay and explained well in Bishop's latest book, "Pattern Recognition and
Machine Learning"
http://research.microsoft.com/~cmbishop/PRML/index.htm<http://research.microsoft.com/%7Ecmbishop/PRML/index.htm>
Yes, the ability to train ANNs in a Bayesian fashion is not new, but it's
also not part of any industry strength package yet.
This would allow you to train without using a hold-out set, and would
automatically handle weight decay and its corresponding parameter.
3. Add support for modeling time-varying (non-stationary) weights. Have
the weights be time dependent. One way to do this is through an extended
Kalman Filter. The following paper shows how to do this for just a single
http://citeseer.ist.psu.edu/penny99dynamic.html
You could extend that, and you could use Bishop's book to get the
derivatives you need to do that.
Just my suggestions!
--Josh

Post by Steffen Nissen
The FANN library has been accepted to participate in Google Summer of Code.
The library is not on the GSoC page ( http://code.google.com/soc/) yet,
but it should be there soon.
So now its up to the students to apply for GSoC.
--
Steffen Nissen - http://MySpace.com/SteffenNissen
Project Administrator - Fast Artificial Neural Network Library (fann)
http://fann.sf.net
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--
Joshua Menke, Ph.D.
Machine Learning Scientist
Trust and Safety Applied Research
ebay, Inc
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