Re: [Fwd: Re: [Databionic-ESOM-User] ESOM]

[<mit...@we...>]

Dear Fabian,
I am facing some problems using eSOM and your help would be very valuable
for me.
I am using eSOM for an .lrn file and when I try to perform training (with
the default parameters) I receive the following message

spheres for 18-tile contain on average 65% of the data searching pareto
radius ...


and finally

at databionics.math.ParetoDensity.getParetoRadius(ParetoDensity.java:156)
at databionics.math.ParetoDensity.getParetoRadius(ParetoDensity.java:323)

The training can not be completed it stops in 95%.

I don't understand what is wrong??
 I suppose that something is wrong with the lrn file used for the training.
I attach the file used for the training.

I would appreciate it if you could help me.

Thanking you in advance.
Katerina


>> @fabian could you please set the reply-to headers for the list?
>
> done
>
>> Katerina Mitrokotsa wrote:
>>
>>>I have recently tried to use ESOm and although I have found it really
>>> interesting I can't understand if there is a way to inspect neuron
>>> values Does this tool permit us to see which samples of data
>>> correspond to which neuron?
>>
>>
>> You can select the samples in the Data tab at the bottom, which are
>> then highlighted.
>
> addition: you can also select neurons in the map with the data mouse
> (activate the leftmost icon in the toolbar). the data points assigned to
> these neurons will be displayed in the data tab at the bottom. you can
> also load a *.names file with text labels for the data points. these
> will be displayed in the last columns of the data table.
>
>>>Furthermore which is the procedure in order to use a dataset for
>>> training and then another dataset for testing.
>>
>> To do this you have to add classmasks to your ESOM and then use the
>> Project tool to see if the test set is projected into the correct
>> classes. The prosses isn't automated as far as i know (fabian?)
>
> creating the class masks is manual (in cvs there is some semi-automated
> support with flood filling already). projection is automated and can be
> run via the menu or the command line. short summary:
>
> - create two seperate *.lrn for training and test data
> - train ESOM with training data
> - optional: load *.cls with known classification of training data
> - identify clusters and create class mask (also *.cls)
> - load *.lrn with test data
> - project this data on ESOM
> - save newly created *.cls for test data
> - optional: analyze *.cls for test data, e.g. compare to *.cls with
> known classification of test data.
>
> we offer no tools for the last step which is rather easy however. i
> could post some matlab code, if you wish.
>
> bye
> fabian
>
>
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