Re: discretization

excuse me, i don't understand well. I go in that site web and then, how can 
i download the file?

----- Original Message ----- 
From: "Peter Reutemann" <fracpete <at> waikato.ac.nz>
To: "Weka machine learning workbench list." 
<wekalist <at> list.scms.waikato.ac.nz>
Sent: Wednesday, February 28, 2007 9:04 PM
Subject: Re: [Wekalist] discretization

>> do you know where i can find this pubblication?
>>
>>  <at> inproceedings{Fayyad1993,
>>    author = {Usama M. Fayyad and Keki B. Irani},
>>    booktitle = {Thirteenth International Joint Conference on Articial 
>> Intelligence},
>>    pages = {1022-1027},
>>    publisher = {Morgan Kaufmann Publishers},
>>    title = {Multi-interval discretization of continuousvalued attributes 
>> for classification learning},
>>    volume = {2},
>>    year = {1993}
>> }
>
> Just get the proceedings:
> http://www.informatik.uni-trier.de/~ley/db/conf/ijcai/ijcai93.html
>
> Cheers, Peter
> -- 
> Peter Reutemann, Dept. of Computer Science, University of Waikato, NZ

Re: discretization

> excuse me, i don't understand well. I go in that site web and then, how
> can
> i download the file?

I haven't seen the paper available for download anywhere, hence the link
with the detailed information (incl ISBN) about the proceedings to get
them in printed form, from a library for instance.

Cheers, Peter
--

-- 
Peter Reutemann, Dept. of Computer Science, University of Waikato, NZ
http://www.cs.waikato.ac.nz/~fracpete/           Ph. +64 (7) 858-5174

meaning of result buffer output in weka

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Useful tips in text categorization?

Hi all,

I'm a software developer and I've been assigned to a text categorization 
project.
I've been reading the literature so far and I'm aware this is quite common, so 
no need to re-invent the wheel.
For example, this post: 
https://list.scms.waikato.ac.nz/mailman/htdig/wekalist/2003-August/001685.html
([Wekalist] Rookie attempting text categorization, +3 years old) is very 
similar to the job I've got to do.

I've got the impression that:

1. IBk (KNN) algorithms are a good choice because: (i) they can easily add new 
documents in the training set, and (ii) can handle sparse vectors(arff files)

2. The principal "con" here is time for execution, but not such a big deal in 
my case.

Anyone done something similar? 

This project in my shop is "R+D", but anyway we don't have so much time to be 
playing around till figuring stuff out.

I've found the Weka project very interesting and exciting, but probably more 
useful to grad students/computer science researcher/etc than industry.

I'm willing to use the Weka libraries and, with your useful help and insights 
get my project ahead.

Of course, I'm willing to share my experience so it gets documented for future  
projects.

So, anyone with useful tips or experiences?

Thanks in advance, and regards,

P.

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Re: Useful tips in text categorization?

Hi 

> I've got the impression that:
> 
> 1. IBk (KNN) algorithms are a good choice because: (i) they can easily add new 
> documents in the training set, and (ii) can handle sparse vectors(arff files)

Naive bayes multinominal can do the same thing. If you want, I can sent my
implementation
to you.SVM can do the same thing(at least in papers),:). 

BTW, anybody is using SVM to do TC?

Jiang Su

RE: Useful tips in text categorization?

Bayesian modeling for text classification has proven to be quite powerful
(see especially the papers at the bottom):
http://psiexp.ss.uci.edu/research/programs_data/toolbox.htm

I saw a c++ implementation of a similar model recently, but can't track it
down now.

Good luck,
Sam

-----Original Message-----
From: wekalist-bounces <at> list.scms.waikato.ac.nz
[mailto:wekalist-bounces <at> list.scms.waikato.ac.nz] On Behalf Of Pablo Siber
Sent: Thursday, March 01, 2007 7:09 AM
To: wekalist <at> list.scms.waikato.ac.nz
Subject: [Wekalist] Useful tips in text categorization?

Hi all,

I'm a software developer and I've been assigned to a text categorization 
project.
I've been reading the literature so far and I'm aware this is quite common,
so 
no need to re-invent the wheel.
For example, this post: 
https://list.scms.waikato.ac.nz/mailman/htdig/wekalist/2003-August/001685.ht
ml
([Wekalist] Rookie attempting text categorization, +3 years old) is very 
similar to the job I've got to do.

I've got the impression that:

1. IBk (KNN) algorithms are a good choice because: (i) they can easily add
new 
documents in the training set, and (ii) can handle sparse vectors(arff
files)

2. The principal "con" here is time for execution, but not such a big deal
in 
my case.

Anyone done something similar? 

This project in my shop is "R+D", but anyway we don't have so much time to
be 
playing around till figuring stuff out.

I've found the Weka project very interesting and exciting, but probably more

useful to grad students/computer science researcher/etc than industry.

I'm willing to use the Weka libraries and, with your useful help and
insights 
get my project ahead.

Of course, I'm willing to share my experience so it gets documented for
future  
projects.

So, anyone with useful tips or experiences?

Thanks in advance, and regards,

P.

_______________________________________________
Wekalist mailing list
Wekalist <at> list.scms.waikato.ac.nz
https://list.scms.waikato.ac.nz/mailman/listinfo/wekalist

Re: meaning of result buffer output in weka

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Dataset limit

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Re: Dataset limit

>   I am using Weka-3-5-3 (on a PC with a Windows 2000 OS). I am processing the input data with VB and import it to
Weka  in Arff format via MS Excel. But now the number of attributes I have is over the 255 column limit of
Excel. What can I do to import the data into Weka?

Weka doesn't have Excel's limit of 255 columns. If you're using VB 
anyway to generate your files, just create CSV files and import/use them 
in Weka. BTW If you upgrade to 3.5.4 or later, you can run, e.g., a 
classifier now from commandline with a CSV file. No need to convert it 
to ARFF first.

The following formats can be used from commandline:
- ARFF (*.arff)
- XRFF (*.xrff) - the new XML-based Weka data format
- CSV (*.csv)
- C4.5 (*.data or *.names)
- libsvm (*.libsvm)
- binary serialized instances (*.bsi)

HTH

Cheers, Peter
--

-- 
Peter Reutemann, Dept. of Computer Science, University of Waikato, NZ
http://www.cs.waikato.ac.nz/~fracpete/           Ph. +64 (7) 858-5174

CHAPTER 14:Embedded Machine Learning

Hi,Where can I find more examples as mentioned in Chapter 14? I want to use the algorithms in my own code.
Thanks   

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