
[Points: 8; Issued: 2004/05/13; Deadline: 2004/05/26; Tutor:
Katharina Seke; Infohour: 2004/05/24, 12:0013:00,
Seminarraum IGI; Einsichtnahme: 2004/06/14, 12:0013:00,
Seminarraum IGI; Download: pdf; ps.gz]
This homework assignment asks you to compare the performance of
three learning algorithms on different data sets by using the WEKA
toolkit^{1}. Choose
three data sets from the archiv contained in the file datasets.zip. Compare the following three
algorithms
Category 
WEKA implementation 
Decision trees 
j48.J48 
Instance based learning 
IBk 
Support vector machines 
SMO 
 Explain your choice of data sets.
 Choose an appropriate method to compare the algorithms.
 Explain what you did so that the results can be reproduced by
everyone.
 Present your results clearly, structured and legibly.
 State for each data set which learning algorithm you would
recommend and explain why. Note: Consider not only the error on the
test set, but also criteria as for instance the time for learning,
interpretability of the hypothesis etc.
Fußnoten
 ^{1}
 See the links section on the CI homepage for further
information and tutorials about WEKA.
