Unified Integration of Explicit Knowledge and
Learning by Example in Recurrent Networks ?
Paolo Frasconi, Student Member, IEEE
Marco Gori, Member, IEEE Marco Maggini
Giovanni Soda, Member, IEEE
We propose a novel unified approach for integrating explicit knowledge and learning
by example in recurrent networks. The explicit knowledge is represented by automaton
rules, which are directly injected into the connections of a network. This
can be accomplished by using a technique based on linear programming, instead of
learning from random initial weights. Learning is conceived as a refinement process
and is mainly responsible of uncertain information management. We present preliminary
results for problems of automatic speech recognition.
Index Terms - Recurrent neural networks, learning automata, automatic speech recognition.
The resurgence of interest in connectionist models has led several researchers to investigate their application to the building of intelligent systems". Unlike symbolic models proposed in artificial intelligence, learning plays a central role in connectionist models. Many successful applications have mainly concerned perceptual tasks (see e.g. [2, 6, 10, 20]),
?This research was partially supported by MURST 40% and CNR Grant 90.01530.CT01. 1The authors are with Dipartimento di Sistemi e Informatica, Universit?a di Firenze, Via di Santa Marta 3 - 50139 Firenze - Italy, Tel. (39) 55-4796265 - Telex 580681 UNFING I - Fax (39) 55-4796363, e-mail : [email protected]