Supervised Sequence Labelling with Recurrent Neural Networks - Couverture rigide

Graves, Alex

9783642247965: Supervised Sequence Labelling with Recurrent Neural Networks

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ISBN 10 : 3642247962 ISBN 13 : 9783642247965

Editeur : Springer-Verlag Berlin and Heidelberg GmbH & Co. K, 2012

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This book offers a complete framework for classifying and transcribing sequential data with recurrent neural networks. It uses state-of-the-art results in speech and handwriting recognition to show the framework in action.

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Les informations fournies dans la section � A propos du livre � peuvent faire r�f�rence � une autre �dition de ce titre.

�diteur: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
Date d'�dition: 2012
Langue: anglais
ISBN 10: 3642247962
ISBN 13: 9783642247965
Reliure: Reli�
Nombre de pages: 146
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Supervised Sequence Labelling with Recurrent Neural Networks

Alex Graves

Edit� par Springer Berlin Heidelberg, 2012

ISBN 10 : 3642247962 ISBN 13 : 9783642247965

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Gebunden. Etat : 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. N� de r�f. du vendeur 5053687

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Supervised Sequence Labelling with Recurrent Neural Networks (Studies in Computational Intelligence, 385)

Graves, Alex

Edit� par Springer, 2012

ISBN 10 : 3642247962 ISBN 13 : 9783642247965

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Supervised Sequence Labelling with Recurrent Neural Networks

Alex Graves

Edit� par Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2012

ISBN 10 : 3642247962 ISBN 13 : 9783642247965

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Buch. Etat : 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. N� de r�f. du vendeur 9783642247965

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Supervised Sequence Labelling with Recurrent Neural Networks

Alex Graves

Edit� par Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2012, 2012

ISBN 10 : 3642247962 ISBN 13 : 9783642247965

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Buch. Etat : 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. N� de r�f. du vendeur 9783642247965

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Supervised Sequence Labelling with Recurrent Neural Networks

Alex Graves

Edit� par Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2012, 2012

ISBN 10 : 3642247962 ISBN 13 : 9783642247965

Neuf Couverture rigide

Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne

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Buch. Etat : 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. N� de r�f. du vendeur 9783642247965

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