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Sinopsis
Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools-robust to input noise and distortion, able to exploit long-range contextual information-that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.
The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video.
Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.
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Acerca del autor
Charlotte y Peter Fiell son dos autoridades en historia, teoría y crítica del diseño y han escrito más de sesenta libros sobre la materia, muchos de los cuales se han convertido en éxitos de ventas. También han impartido conferencias y cursos como profesores invitados, han comisariado exposiciones y asesorado a fabricantes, museos, salas de subastas y grandes coleccionistas privados de todo el mundo. Los Fiell han escrito numerosos libros para TASCHEN, entre los que se incluyen 1000 Chairs, Diseño del siglo XX, El diseño industrial de la A a la Z, Scandinavian Design y Diseño del siglo XXI.
De la contraportada
Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools--robust to input noise and distortion, able to exploit long-range contextual information--that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.
The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video.
Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.
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Supervised Sequence Labelling with Recurrent Neural Networks
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Gebunden. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Recent research in Supervised Sequence Labelling with Recurrent Neural Networks New results in a hot topic Written by leading expertsSupervised sequence labelling is a vital area of machine learning, encompassing tasks such as sp. Nº de ref. del artículo: 5053687
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Supervised Sequence Labelling with Recurrent Neural Networks (Studies in Computational Intelligence, 385)
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Supervised Sequence Labelling with Recurrent Neural Networks
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools-robust to input noise and distortion, able to exploit long-range contextual information-that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition. 160 pp. Englisch. Nº de ref. del artículo: 9783642247965
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Supervised Sequence Labelling with Recurrent Neural Networks
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Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2012
ISBN 10: 3642247962
ISBN 13: 9783642247965
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools-robust to input noise and distortion, able to exploit long-range contextual information-that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary. The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal. Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video. Experimental validation is provided by state-of-the-art results in speech and handwriting recognition. Nº de ref. del artículo: 9783642247965
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Supervised Sequence Labelling with Recurrent Neural Networks (Studies in Computational Intelligence, 385)
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Supervised Sequence Labelling with Recurrent Neural Networks
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Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2012, 2012
ISBN 10: 3642247962
ISBN 13: 9783642247965
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Buch. Condición: Neu. Neuware -Supervised sequence labelling is a vital area of machine learning, encompassing tasks such as speech, handwriting and gesture recognition, protein secondary structure prediction and part-of-speech tagging. Recurrent neural networks are powerful sequence learning tools¿robust to input noise and distortion, able to exploit long-range contextual information¿that would seem ideally suited to such problems. However their role in large-scale sequence labelling systems has so far been auxiliary.The goal of this book is a complete framework for classifying and transcribing sequential data with recurrent neural networks only. Three main innovations are introduced in order to realise this goal.Firstly, the connectionist temporal classification output layer allows the framework to be trained with unsegmented target sequences, such as phoneme-level speech transcriptions; this is in contrast to previous connectionist approaches, which were dependent on error-prone prior segmentation. Secondly, multidimensional recurrent neural networks extend the framework in a natural way to data with more than one spatio-temporal dimension, such as images and videos. Thirdly, the use of hierarchical subsampling makes it feasible to apply the framework to very large or high resolution sequences, such as raw audio or video.Experimental validation is provided by state-of-the-art results in speech and handwriting recognition.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 160 pp. Englisch. Nº de ref. del artículo: 9783642247965
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Supervised Sequence Labelling with Recurrent Neural Networks (Studies in Computational Intelligence, 385)
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Supervised Sequence Labelling with Recurrent Neural Networks
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Supervised Sequence Labelling With Recurrent Neural Networks
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ISBN 13: 9783642247965
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