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
