Toward the border between neural and Markovian paradigms
Wilinski, P.
Solaiman, B.
Hillion, A.
Czarnecki, W.
Ecole Nat. Superieure des Telecommun. de Bretagne, Brest;
This paper appears in: Systems, Man, and Cybernetics, Part B: Cybernetics, IEEE Transactions on
Publication Date: Apr 1998
Volume: 28,
Issue: 2
On page(s): 146-159
ISSN: 1083-4419
References Cited: 67
CODEN: ITSCFI
INSPEC Accession Number: 5885441
Digital Object Identifier: 10.1109/3477.662756
Current Version Published: 2002-08-06
Abstract
A new tendency in the design of modern signal processing methods
is the creation of hybrid algorithms. This paper gives an overview of
different signal processing algorithms situated halfway between
Markovian and neural paradigms. A new systematic way to classify these
algorithms is proposed. Four specific classes of models are described.
The first one is made up of algorithms based upon either one of the two
paradigms, but including some parts of the other one. The second class
includes algorithms proposing a parallel or sequential cooperation of
two independent Markovian and neural parts. The third class tends to
show Markov models (MMs) as a special case of neural networks (NNs), or
conversely NNs as a special case of MMs. These algorithms concentrate
mainly on bringing together respective learning methods. The fourth
class of algorithms are hybrids, neither purely Markovian nor neural.
They can be seen as belonging to a more general class of models,
presenting features from both paradigms. The first two classes
essentially include models with structural modifications, while two
later classes propose algorithmic modifications. For the sake of
clarity, only main mathematical formulas are given. Specific
applications are intentionally avoided to give a wider view of the
subject. The references provide more details for interested
readers
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