  Document classification is a task of assigning a new unclassified document to
one of the predefined set of classes. The content based document classification
uses the content of the document with some weighting criteria to assign it to
one of the predefined classes. It is a major task in library science,
electronic document management systems and information sciences. This paper
investigates document classification by using two different classification
techniques (1) Support Vector Machine (SVM) and (2) Relevance Vector Machine
(RVM). SVM is a supervised machine learning technique that can be used for
classification task. In its basic form, SVM represents the instances of the
data into space and tries to separate the distinct classes by a maximum
possible wide gap (hyper plane) that separates the classes. On the other hand
RVM uses probabilistic measure to define this separation space. RVM uses
Bayesian inference to obtain succinct solution, thus RVM uses significantly
fewer basis functions. Experimental studies on three standard text
classification datasets reveal that although RVM takes more training time, its
classification is much better as compared to SVM.
