Toward the border between neural and Markovian paradigms

— 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 (MM’s) as a special case of neural networks (NN’s), or conversely NN’s as a special case of MM’s. 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 fea...

• Type: Journal
• Year: 1998
• Where: TSMC
• Authors: Piotr Wilinski, Basel Solaiman, A. Hillion, W. Czarnecki
• Keywords: Algorithms, Neural, Special Case, TSMC 1998