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Support vector machines for improved multiaspect target recognition using the Fisher kernel scores of hidden Markov models
2002
IEEE International Conference on Acoustics Speech and Signal Processing
In conjunction with physics-based feature extraction, Hidden Markov Model (HMM) classifiers have been used successfully to fuse scattering data from multiple target orientations where the target-sensor orientation is generally unknown or "hidden" [1]. The use of prior knowledge concerning sensor motion is employed in modeling the sequential data, improving classification performance. However, the assumptions of first order Markovian state transitions state-dependent statistics constrain the
doi:10.1109/icassp.2002.1005315
fatcat:2iuvmf7cbnbg7pjk3xjenceree