Localized support vector machines using Parzen window for incomplete sets of categories

This paper describes a novel approach to pattern classification that combines Parzen window and support vector machines. Pattern classification is usually performed in universes where all possible categories are defined. Most of the current supervised learning classification techniques do not account for undefined categories. In a universe that is only partially defined, there may be objects that do not fall into the known set of categories. It would be a mistake to always classify these objects as a known category. We propose a Parzen window-based approach which is capable of classifying an object as not belonging to a known class. In our approach we use a Parzen window to identify local neighbors of a test point and train a localized support vector machine on the identified neighbors. Visual category recognition experiments are performed to compare the results of our approach, localized support vector machines using a k-nearest neighbors approach, and global support vector machines. Our experiments show that our Parzen window approach has superior results when testing with incomplete sets, and comparable results when testing with complete sets.