Symbolic knowledge and neural networks: insertion, refinement and extraction
Symbolic knowledge and neural networks: insertion, refinement and extraction
Fundamentals of speech recognition
Fundamentals of speech recognition
Speech recognition using neural networks
Speech recognition using neural networks
Speech recognition: theory and C++ implementation
Speech recognition: theory and C++ implementation
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Connectionist-Symbolic Integration: From Unified to Hybrid Approaches
Connectionist-Symbolic Integration: From Unified to Hybrid Approaches
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Connectionist models often offer good performance in pattern recognition and generalization, and present such qualities as natural learning ability, noise tolerance and graceful degradation. By contrast, symbolic models often present a complementary profile: they offer good performance in reasoning and deduction, and present such qualities as natural symbolic manipulation and explanation abilities. In the context of this paper, we address two limitations of artificial neural networks: the lack of explicit knowledge and the absence of temporal aspect in their implementation. STN : is a model of a specialized temporal neuron which includes both symbolic and temporal aspects. To illustrate the STN utility, we consider a system for phoneme recognition.