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Issue No.02 - April (1995 vol.7)
pp: 340-346
<p><it>Abstract</it>—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.</p>
Recurrent neural networks, learning automata, automatic speech recognition.
Marco Gori, Marco Maggini, Paolo Frasconi, "Unified Integration of Explicit Knowledge and Learning by Example in Recurrent Networks", IEEE Transactions on Knowledge & Data Engineering, vol.7, no. 2, pp. 340-346, April 1995, doi:10.1109/69.382304
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