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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I would like to know the number of selected support vectors in a SVM after
a certain learning on a certain dataset. I didn't find any method to get
this information. Is it availaible ? Would it be possible to add it easily
I want to compare Weka's SMO against SVMLight.
I'm trying to convert a DB in Weka's ARFF format to
My problem is that I don't know how to convert it.
I looked everywhere without a success.
Can someone please help me?
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I'm doing some experiments where the confidence score is relevant to how I
treat the prediction. In a two class problem, I want to consider 0.6
confidence of 'False' as 0.4 confidence of 'True', and then sort the
instances by their confidence score.
When evaluating algorithms on such a task, should I be considering anything
but the classification accuracy? Rule and decision tree learners have been
giving me the best classification accuracy (PART has been the best), but
I'm unsure about how they assign their confidence scores.
Any advice would be greatly appreciated.
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You can solve the out of memory error by giving larger heap size to the Java vitual machine. Try:
java -Xmx400m .........
The above would try to give 400MB to the virtual machine. You can ofcourse give a different heap size according to the amount of memory you have installed on your machine. Also if possible try to run memory intensive tasks using command line instead of the gui. This also saves quite a lot of memory.
If you give 300-400MB in heap size to JVM and run the classifiers using command line then there shouldn't be any problem with datasets with number of attributes/features as large as 100,000, with tens of thousands of instances/examples. At least that's what I've tried and worked for me.
Please feel free to write if you have any further queries.
I am using weka classification package on Reuters Mod-apte split. I have
preprocessed the text and store the document vectors in the arff file.
However, the data file is very large. I have tried to reduce the dimensions
using document frequency threshold ( the number of documents contain the
attributes, I set it to 100). There are about 800 dimensions left but I
still can not run the classification algorithm on it. I have tried j48 and
smo and they both gave me out of memory error. I don't know what to do to
make it work. If anyone can give me some advice, I will really appreciate it.