Re: advanced Weka segmentation

Posted by Anda Cornea on Feb 06, 2013; 5:34pm
URL: http://imagej.273.s1.nabble.com/Re-advanced-Weka-segmentation-tp5001680p5001689.html

Dear Ignacio, thank you so much!  Both the pdf and your info are quite helpful.

Thanks again,

Anda



-----Original Message-----
From: ImageJ Interest Group [mailto:[hidden email]] On Behalf Of Ignacio Arganda-Carreras
Sent: Tuesday, February 05, 2013 7:10 PM
To: [hidden email]
Subject: Re: advanced Weka segmentation

Dear Anda,

I can only think on this old survey from 1997:
http://machine-learning.martinsewell.com/feature-selection/DashLiu1997.pdf

In general, feature selection helps reducing the amount of memory to use during the training and the posterior classification. In AWS, the default classifier is a random forest, which works very well even in the presence of too many non-informative features. If you have enough RAM, my advice is that you start playing around with as many features as possible and then keep reducing them to select the best of them.

The Weka explorer has an option to perform feature selection as well.

I hope this helps!

ignacio




On Tue, Feb 5, 2013 at 4:52 PM, Anda Cornea <[hidden email]> wrote:

> Hello!
>
> I am a very happy end user of the weka segmentation with very limited
> understanding of the algorithm.  Can anyone recommend any easy reading
> of general concepts and practical implications of the large choice of
> training features?
>
> Thank you in advance,
>
> Anda
>
>
>
> Anda Cornea, PhD
> Director of the Imaging and Morphology Support Core Oregon National
> Primate Research Center Oregon Health & Science University
> 503-690-5293
>
> --
> ImageJ mailing list: http://imagej.nih.gov/ij/list.html
>

--
Ignacio Arganda-Carreras, Ph.D.
Seung's lab, 46-5065
Department of Brain and Cognitive Sciences Massachusetts Institute of Technology
43 Vassar St.
Cambridge, MA 02139
USA

Phone: [phone]
Website: http://bioweb.cnb.csic.es/~iarganda/index_EN.html

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