1 research outputs found
Kernel-based Generative Learning in Distortion Feature Space
This paper presents a novel kernel-based generative classifier which is
defined in a distortion subspace using polynomial series expansion, named
Kernel-Distortion (KD) classifier. An iterative kernel selection algorithm is
developed to steadily improve classification performance by repeatedly removing
and adding kernels. The experimental results on character recognition
application not only show that the proposed generative classifier performs
better than many existing classifiers, but also illustrate that it has
different recognition capability compared to the state-of-the-art
discriminative classifier - deep belief network. The recognition diversity
indicates that a hybrid combination of the proposed generative classifier and
the discriminative classifier could further improve the classification
performance. Two hybrid combination methods, cascading and stacking, have been
implemented to verify the diversity and the improvement of the proposed
classifier.Comment: 29 pages, 7 figure