[GRASS-dev] grass classification routines and small / large numbers

[Markus Neteler]

Dylan Beaudette-2 wrote:
> 
> On Thursday 28 June 2007 06:42, Markus Neteler wrote:
>> Dylan Beaudette wrote on 06/26/2007 07:33 PM:
>> > Hi,
>> >
>> > I have found that using the GRASS classification modules work well when
>> > the inputs come from discreet (0-255) distributions- for example
>> landsat
>> > channels, etc. - however I seem to get a lot of singularity problems,
>> or
>> > maps with a single class when using floating point values of different
>> > magnitude.
>>
>> Dylan, *,
>>
>> there is i.pr available in the GRASS Addons repository from Stefano
>> Merler (my colleague from IRST):
>> https://grasssvn.itc.it/grasssvn/grassaddons/trunk/grassaddons/
>>
>> "
>>  * pr : C code for classification problems. It implements k-NN
>> (multiclass), classification trees (multiclass), maximum likelihood
>> (multiclass), Support Vector Machines (binary), bagging versions of all
>> the base classifiers, AdaBoost for binary trees and support vector
>> machines. It allows feature manipulation (normalization, principal
>> components,...). It also implements feature selection techniques (RFE,
>> E-RFE,...), statistical tests on variables, tools for resampling
>> (cross-validation and bootstrap) and cost-sensitive techniques for trees
>> and support vector machines. Feature selection techniques and
>> statistical tests are not distributed in the current release.
>>
>> ||| * i.pr : a version of pr implemented in the GIS GRASS for dealing
>>
>> with images.
>>
>> |"
>>
>> Maybe interesting?
>> Markus
>>
> 
> This might be just the ticket. I was hoping to avoid a constant jump back
> and 
> forth between GRASS and R (although it may be worth it...?) -- so the i.pr 
> routines sound great. Any word on weather or not this module will end up
> in 
> the main distribution of GRASS ? Perhaps I can do some testing and report 
> back. 
> 

Yes, would be great. After a good testing period it should go into the main
distro.

Markus
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