Artificial neural networks for speech and vision
著者
書誌事項
Artificial neural networks for speech and vision
(Chapman & Hall neural computing, 4)
Chapman & Hall, 1994
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注記
Includes bibliographical references and index
内容説明・目次
内容説明
The author has brought together a group of contributors to produce a volume that presents work in the field of neural networks for sppech processing and computer vision. The text seeks to integrate the information available at the moment and should be useful among those engaged in neural network research and applications. This book should be of interest to graduate students, academic and professional researchers.
目次
Neural Networks for speech processing. Neural Networks in the acquisition of speech by machine. The nervous system - fantasy and reality. Processing of complex stimuli in the mammalian cochlear nucleus. On the possible role of auditory peripheral feedback in the representation of speech sounds. Is there a role for neural networks in speech recognition? The neurosphysiology of world reading - a connectionist approach. Status versus stacks - representing grammatical strucure in a recurrent neural network. Connections and associations in language acquisition. Some relationships between artificial neural nets and hidden markov models. A learning neural tree for phoneme classification. Decision feedback learning of neural networks. Visual focus of attention in language acquisition. Integrated segmentation and recognition of handprinted characters. Neural net image analysis for postal applications - from locating address blocks to determining zip codes. Space invariant active vision. Engineering document processing with neural networks. Goal-oriented training of neural networks. Hybrid neural networks and image restoration. Dynamic systems and perception. Deterministic annealing for optimization. Neural networks in vision. A neural chip set for supervised learning and CAM. A discrete radon transform method for invariant image analysis using artificial neural networks. Recurrent neural networks and sequential machines. Non-literal transfer of information among inductive learners. Neural networks for identification and control of nonlinear systems. Using neural networks to identify DNA sequences.
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