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Publications (4)4.63 Total impact

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    K. T. Abou-Moustafa, M. Cheriet, C. Y. Suen
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    ABSTRACT: Classification of Time-Series 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 modeling Time-Series data due to their efficiency. This paper proposes a general generative/discriminative hybrid that uses HMMs to map the variable length Time-Series data into a fixed P-dimensional vector that can be easily classified using any discriminative model. The hybrid system was tested on the MNIST database for unconstrained handwritten numerals and has achieved an improvement of 1.23% (on the test set) over traditional 2D discrete HMMs.
    Ninth International Workshop on Frontiers in Handwriting Recognition. 11/2004;
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    K.T. Abou-Moustafa, C.Y. Suen, M. Cheriet
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    ABSTRACT: Classification of sequential data using discriminative models such as support vector machines 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%).
    Acoustics, Speech, and Signal Processing, 2004. Proceedings. (ICASSP '04). IEEE International Conference on; 06/2004
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    K. T. Abou-Moustafa, C. Y. Suen
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    ABSTRACT: Classification of Sequential data using discriminative mod - els 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 represent- ing sequential data due to their efficiency. This paper pro- poses a general generative-discriminative framework that uses HMMs to map the variable length sequential data into a fixed size -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 recog- nition rate of 98.02% than that of standard HMMs (94.19%).
    01/2004;
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    K.T. Abou-Moustafa, M. Cheriet, C.Y. Suen
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    ABSTRACT: This paper investigates the effect of HMM structure on the performance of HMM-based classifiers. The investigation is based on the framework of graphical models, the diffusion of credits of HMMs and empirical experiments. Although some researchers have focused on determining the number of states, this study shows that the topology has a stronger influence on increasing the performance of HMM-based classifiers than the number of states.
    Pattern Recognition Letters. 01/2004;