Hi,
 
How did you choose the parameters
(such as sigma^2 or the degree of the polynomial) ?
 
-- A.
 
--------------------------------
Dr. Andrea Boni
University of Trento
Faculty of Engineering
Via Mesiano, 77
I-38050 Povo (TN), Italy.
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tel   : +39-0461-882440
fax   : +39-0461-881977
email : [log in to unmask]
http://leoesd.ing.unitn.it/
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----- Original Message -----
From: [log in to unmask]>SatPrem Reddy
To: [log in to unmask]>[log in to unmask]
Sent: Monday, October 01, 2001 8:25 PM
Subject: analyze the performance of svm - urgent

Hi all,
    I am currently writing a paper on comparison of SVMs, neural nets and 
KNN classifiers for digit recognition when the training set is very small. We
have created our own dataset, with 250 training samples and 2047 testing
samples. In my experiments, I observed that while linear SVM gives an
accuracy of 81.44%, polynomial, RBF and sigmoid kernels give accuracy
of about 20%. I am unable to understand why there is such a big difference
in their performance. If somebody could clarify why this is happening, I'd be
glad.
-P.SatPrem Reddy