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Summary: IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 16, NO. 2, MARCH 2005 447
Multi-Aspect Target Discrimination Using Hidden
Markov Models and Neural Networks
Marc Robinson, Mahmood R. Azimi-Sadjadi, Senior Member, IEEE, and Jaime Salazar
Abstract--This paper presents a new multi-aspect pattern
classification method using hidden Markov models (HMMs).
Models are defined for each class, with the probability found by
each model determining class membership. Each HMM model is
enhanced by the use of a multilayer perception (MLP) network to
generate emission probabilities. This hybrid system uses the MLP
to find the probability of a state for an unknown pattern and the
HMM to model the process underlying the state transitions. A new
batch gradient descent-based method is introduced for optimal es-
timation of the transition and emission probabilities. A prediction
method in conjunction with HMM model is also presented that
attempts to improve the computation of transition probabilities
by using the previous states to predict the next state. This method
exploits the correlation information between consecutive aspects.
These algorithms are then implemented and benchmarked on
a multi-aspect underwater target classification problem using a
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