A GENERATIVE-DISCRIMINATIVE HYBRID FOR SEQUENTIAL DATA CLASSIFICATION

Karim Abou-Moustafa, Ching Suen, Mohamed Cheriet

Abstract: Classification of Sequential data using discriminative models such as SVMs is very hard due to the variable length of this type of data. On the other hand, generative models such as HMMs have become the standard tool for representing sequential data due to their efficiency. This paper proposes a general generative-discriminative framework that uses HMMs to map the variable length sequential data into a fixed size p-dimensional vector (likelihood score) that can be easily classified using any discriminative model. The preliminary experiments of the framework on the MNIST database for handwritten digits have achieved a better recognition rate of 98.02% than that of standard HMMs (94.19%).