Edité par Springer Berlin Heidelberg, 2014
ISBN 10 : 3642432182 ISBN 13 : 9783642432187
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Ajouter au panierEtat : Sehr gut. Zustand: Sehr gut | Seiten: 160 | Sprache: Englisch | Produktart: Bücher.
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Vendeur : Ria Christie Collections, Uxbridge, Royaume-Uni
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Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2014
ISBN 10 : 3642432182 ISBN 13 : 9783642432187
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
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Ajouter au panierTaschenbuch. 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.
Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg, 2012
ISBN 10 : 3642247962 ISBN 13 : 9783642247965
Langue: anglais
Vendeur : AHA-BUCH GmbH, Einbeck, Allemagne
EUR 192,59
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Ajouter au panierBuch. 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.
Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2012, 2012
ISBN 10 : 3642247962 ISBN 13 : 9783642247965
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 192,59
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Ajouter au panierBuch. 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.
Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg Apr 2014, 2014
ISBN 10 : 3642432182 ISBN 13 : 9783642432187
Langue: anglais
Vendeur : buchversandmimpf2000, Emtmannsberg, BAYE, Allemagne
EUR 192,59
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Ajouter au panierTaschenbuch. 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.
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Ajouter au panierEtat : New. pp. xiv + 146.
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Edité par Springer-Verlag New York Inc, 2014
ISBN 10 : 3642432182 ISBN 13 : 9783642432187
Langue: anglais
Vendeur : Revaluation Books, Exeter, Royaume-Uni
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Ajouter au panierPaperback. Etat : Brand New. 2012 edition. 160 pages. 9.25x6.10x0.47 inches. In Stock.
Edité par Springer-Verlag New York Inc, 2012
ISBN 10 : 3642247962 ISBN 13 : 9783642247965
Langue: anglais
Vendeur : Revaluation Books, Exeter, Royaume-Uni
EUR 269,15
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Ajouter au panierHardcover. Etat : Brand New. 2012 edition. 160 pages. 9.50x6.50x0.75 inches. In Stock.
Vendeur : Mispah books, Redhill, SURRE, Royaume-Uni
EUR 273,26
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Edité par Springer Berlin Heidelberg, 2012
ISBN 10 : 3642247962 ISBN 13 : 9783642247965
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 162,51
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Ajouter au panierGebunden. 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.
Edité par Springer Berlin Heidelberg, 2014
ISBN 10 : 3642432182 ISBN 13 : 9783642432187
Langue: anglais
Vendeur : moluna, Greven, Allemagne
EUR 162,51
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Ajouter au panierEtat : 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.
Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg Feb 2012, 2012
ISBN 10 : 3642247962 ISBN 13 : 9783642247965
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 192,59
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Ajouter au panierBuch. 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.
Edité par Springer Berlin Heidelberg, Springer Berlin Heidelberg Apr 2014, 2014
ISBN 10 : 3642432182 ISBN 13 : 9783642432187
Langue: anglais
Vendeur : BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Allemagne
EUR 192,59
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Ajouter au panierTaschenbuch. 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.
Vendeur : Majestic Books, Hounslow, Royaume-Uni
EUR 264,68
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Ajouter au panierEtat : New. Print on Demand pp. xiv + 146 62 Illus. (12 Col.).
Vendeur : Majestic Books, Hounslow, Royaume-Uni
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Ajouter au panierEtat : New. Print on Demand pp. 160 62 Illus. (12 Col.).
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
EUR 271,08
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Ajouter au panierEtat : New. PRINT ON DEMAND pp. xiv + 146.
Vendeur : Biblios, Frankfurt am main, HESSE, Allemagne
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Ajouter au panierEtat : New. PRINT ON DEMAND pp. 160.