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Today's Topics:

   1. Adding a new Filter---Jar generation (Cristina Torres)
   2. gridregression.py (M. Fatih Akay)
   3. Re: Multilayerperceptron results (Harri M.T. Saarikoski)
   4. RE: Re:PATH variable (Danilovich, Yann)


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>From lmjznprcyynlydlf@hotmail.com  Sun Feb 24 10:27:37 2008
Message: 1
Date: Fri, 8 Feb 2008 10:37:01 +0000
From: Cristina Torres 
Subject: [Wekalist] Adding a new Filter---Jar generation
To: Weka machine learning workbench list.
	
Message-ID: 
Content-Type: text/plain; charset="iso-8859-1"







Hello   Finally I solve the problem of the compilation of Weka, and I also introduce the new filter (filter.java), in the Filters folders, Now I want to create the .jar document of the complete Weka with my new filter, I generate the jar file and when I try to execute it have this error:
 
Failed to lad Main Class manifiest atribute 
 
Somebody knows how I have to generate this .jar file properly?
 
 
Cris



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>From kylwi0776@yahoo.com  Sun Feb 24 10:27:37 2008
Message: 2
Date: Fri, 8 Feb 2008 03:24:11 -0800 (PST)
From: "M. Fatih Akay" 
Subject: [Wekalist] gridregression.py
To: weka 
Message-ID: <641465.488.qm@web52802.mail.re2.yahoo.com>
Content-Type: text/plain; charset="iso-8859-1"

Mark/Peter,
   
  Is it possible to integrate gridregression.py (or a similar tool), which is a tool under LIBSVM for finding the optimized parameters of c, gamma and epsilon, into WEKA?
   
  Fatih

       
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>From grelj.zktpquktdd@helsinki.fi  Sun Feb 24 10:27:37 2008
Message: 3
Date: Fri,  8 Feb 2008 14:58:43 +0200
From: "Harri M.T. Saarikoski" 
Subject: Re: [Wekalist] Multilayerperceptron results
To: "Weka machine learning workbench list."
	
Message-ID: <1202475523.47ac5203bf83d@webmail.helsinki.fi>
Content-Type: text/plain; charset=us-ascii

> But every once in a while I find a combination of parameters where the
> classification doesnt work at all and one (sometimes even two) class(es)
> seems to be left out of the process and the confusion matrix looks like
> the following:
> 
>     a    b    c   <-- classified as
>     0 6838    4 |    a = '(-inf-0.25]'
>     0 6728   72 |    b = '(0.25-0.75]'
>     0 2141 4952 |    c = '(0.75-inf)'
> 
> 
> What can be the reason for such behaviour?

Hi. 

Most likely this is a case of overfitting (overtraining): 
(http://en.wikipedia.org/wiki/Overfitting): "especially in cases where 
learning was performed too long or where training examples are rare, the 
learner may adjust to very specific random features of the training data, 
that have no causal relation to the target function." 

Overfitting in back-propagation neural networks like MLP (resulting in 
prediction bias toward one/some of the target classes) can be caused by the 
following: if misclassified training instances continue to be misclassified 
iteration after iteration, they get overweighted and actually take weight 
from the correctly classified instances, i.e. the weights become 
imbalanced. Furthermore, if misclassified instances happen to be clearly 
concentrated to some classes, this can result in confusion matrix that you 
quoted (zero correct instances for some classes). 

> I have not found a pattern in the parameters that causes this. 

MLP describes the parameters as follows: "learningRate -- The amount the 
weights are updated. momentum -- Momentum applied to the weights during 
updating. decay -- This will cause the learning rate to decrease. This will 
divide the starting learning rate by the epoch number, to determine what 
the current learning rate should be. This may help to stop the network from 
diverging from the target output, as well as improve general performance." 

As far as I know, no off-the-shelf formula exists for optimizing these 
parameters. It is an interchange of parameters and the dataset. If you are 
looking for a pattern in performance, consider that the parameters operate 
together (learning rate * momentum / decay) so that the first two operate 
for controlling speed of change and the latter for stopping the change. 

"Ultimately, the cost function will depend on the task we wish to perform." 
(wiki). If you are getting as high as > 85% accuracies from most 
configurations, this suggests that optimal training model for this dataset 
is produced when the parameters are set on 'fast burn' (few iterations) so 
I would say higher learning rate * momentum and fast decay work best (do 
they?). 

Harri

> 
> David
> 
> 
> 


------------------
yst. terv | Best wishes
Harri Saarikoski



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