Introduction To Support Vector Machines
Institute for Signal and Information Processing
Mississippi State University
Department of Electrical and Computer Engineering
phone/fax: 601-325-3149; email: ganapath@isip.msstate.edu
URL:
/research/isip/resources
We have seen during our previous seminars how classical statistical
techniques like Linear Discriminant Analysis (LDA) and Principal
Component Analysis are used in solving classification problems. They
however have restricted performance when the decision surfaces required
are non-linear. So, when data sets are inherently separated by a non-linear
decision surface, it is advantageous to use a non-linear classifier.
Neural Networks and K-NN classifier are some of the widely used
non-linear classifiers.
In this talk, we introduce a new classification scheme called the Support
Vector Machine (SVM) which has gained prominence in the past couple of
years. It has some interesting features, like control over generalizability,
maximum expected error and most importantly has a discriminative
rather than a representative character. We present preliminary results
of using SVMs to classify 11 vowels on a standard non-linear classification
data set.