Unified Integration of Explicit Knowledge and Learning by Example in Recurrent Networks

Authors:
Paolo Frasconi;Marco Gori;Marco Maggini;Giovanni Soda
Affiliations:
-;-;-;-
Venue:
IEEE Transactions on Knowledge and Data Engineering
Year:
1995

Citing 6
Cited 12

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References

Intelligent data analysis
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Diffusion of context and credit information in Markovian models

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An Adaptive Learning Algorithm for Supervised Neural Network with Contour Preserving Classification

AICI '09 Proceedings of the International Conference on Artificial Intelligence and Computational Intelligence
A dual network adaptive learning algorithm for supervised neural network with contour preserving classification for soft real time applications

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Inductive bias strength in knowledge-based neural networks: application to magnetic resonance spectroscopy of breast tissues

Artificial Intelligence in Medicine
A computational framework based on behavioural modelling: Application to the matching of electrocardiogram (ECG) recordings

Mathematical and Computer Modelling: An International Journal

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Abstract

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 for uncertain information management. We present preliminary results for problems of automatic speech recognition.