I can run it on Win2k Client.
I used the following command:
C:\DJFHQ\Info_Extraction\Weka>java -classpath weka-3-2-3\weka-3-2-3\weka.jar
I have WekaMetal.jar in the CLASSPATH. I also used WinZip to "unzip" the JAR
files for Weka 3.2.3 and WekaMetal after downloading them.
My version of Java is the following:
java version "1.3.0_02"
Java(TM) 2 Runtime Environment, Standard Edition (build 1
Java HotSpot(TM) Client VM (build 1.3.0_02, mixed mode)
PS I also noticed a possible typo in your quoted string which maybe should
"G:\Program Files\Weka-3-2-3\weka.jar" i.e. the "-3" is missing.
From: Chris Bacon [mailto:email@example.com]
Sent: Saturday, 17 August 2002 10:29 AM
Subject: [Wekalist] WekaMetal and Windows?
Has anyone been able to run WekaMetal on Windows? I've tried on
Server from a command prompt where I get this:
G:\Program Files\WekaMetal>java -jar WekaMetal.jar:"G:\Program
Exception in thread "main" java.util.zip.ZipException: The filename,
ame, or volume label syntax is incorrect
at java.util.zip.ZipFile.open(Native Method)
at java.util.zip.ZipFile.<init>(Unknown Source)
at java.util.jar.JarFile.<init>(Unknown Source)
at java.util.jar.JarFile.<init>(Unknown Source)
I've played around with the ClassPath, but it didn't help. I've also
from within IBM Websphere Application Developer, where it gets an IO error
because it can't find the cache files (ap.cache and dc.cache), even though
they're both in the same directory as the WekaMetal.jar file.
Any help would be appreciated.
Wekalist mailing list
I am applying ID3 to a medical data set and was wondering if there is a way to
get Weka to output the results of the ID3 algorithm graphically so that the
Specialist can interpret the results easily?
I have written a classification scheme in Java using the Weka-library.
Now, I want to use the 'Experimenter' for doing some experiments
with this new classification scheme.
(Up till now I wrote my own experiments in Java, but probably the
Experimenter seems like a better option.)
In the README file it is indicated that the file
GenericObjectEditor.props is the place to be. I thus copied this
file to my home directory and I added ...
monotone.MinMaxExtension,\ #line added
monotone.OSDL #line added
This works, in the sense that in the Experimenter-gui
I now can choose from 'MinMaxExtension' and 'OSDL'.
but upon choosing one of these I get the error message
'could not create an example of weka.MinMaxExtension from the
current classpath' ...
I have to say that
1) I find it strange that the 'monotone' part is cut from the classname,
and that 'weka' is prefixed ...
2) the class 'MinMaxExtension' is part of the package 'monotone'.
I.e. the first line of 'MinMaxExtension.java' is 'package monotone;'
3) the file 'MinMaxExtension.class' is in a directory called
$SOMETHING/Source/monotone and this directory is part of my
classpath; in my .bashrc I have
Any hints on how to proceed are welcome. If possible I would like to
keep these files in the current package ....
I would like to know whether someone has written/developed an Independent Component Analysis Module For Weka.
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On Oct 15, 2004, at 12:18 PM, wekalist-request(a)list.scms.waikato.ac.nz
> I compared the results of SMO with LIBSVM
> (http://www.csie.ntu.edu.tw/~cjlin/libsvm/), which is a simplication
> of both
> SMO(Platt) and SVMLIGHT(Joachims) using the RBF kernel with same c, G,
> tolarence parameter, and 10-fold cross validation. With LIBSVM I got
> the 81.44%
> accuracy and with SMO I got 67.14%. Why are they so different?
There can be multiple reasons for this (note that I don't know anything
a) SMO normalizes the attributes (but you can turn that off)
b) The dataset is small and if you repeat the cross-validation with a
different random number seed you get a very different result (try
changing the seed). It is very unlikely that both LIBSVM and Weka
happen to shuffle the data so that the cross-validation folds are
c) There is a bug somewhere in Weka's SMO (seems unlikely).
> as the value of C increases, the algorithm should try to classify the
> more accurately.
This is incorrect (if you are referring to cross-validation). As you
increase C (and allow the algorithm to fit the training data more
closely) the accuracy on the TRAINING DATA normally goes up. However,
this might lead to overfitting, which would mean the cross-validated
error would go up.
> But that's not happening in SMO. After 1000, it is
> decreasing!! why? I ran the SMO with different data, but I am always
> the optimal value for C within 100-500 range.
>----- Oorspronkelijk bericht -----
Rajni jain wrote:
> Hello all,
> Although the question is not under the purview of the list, yet I hope
> somebody would definitely know- how to merge two or more PDF files. What
> all softwares are available for this purpose?
> Is any free ware also available?
Have a look at pdftk.
It luckily is no freeware, but free software !!
>Wekalist mailing list
I have a question about the return value of classification.
When I predict an instance:
class is an integer like 0, 1, 2, ...
For example, if there are 4 classes 'a', 'b', 'c', 'd'. So, what is
the map relationship between 'a', 'b', 'c', 'd' and 0, 1, 2, 3? 'a'=0
Thanks a lot!
No, there aren't any plans at the moment. However, it would be very
nice to have an incremental learner for SVMs in Weka...
On Dec 1, 2004, at 12:32 PM, wekalist-request(a)list.scms.waikato.ac.nz
> Thanks. I also found this paper with google. It seems to be the only
> paper with an empirical evaluation of the performance of an
> incremental version of SVM.
> Does the weka team plan to implement one ?
> Best regards,
> Nicolas Saunier