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Supervised Sequence Labelling with Recurrent Neural Networks - Alex Graves

Supervised Sequence Labelling with Recurrent Neural Networks

(Autor)

Buch | Softcover
XIV, 146 Seiten
2014
Springer Berlin (Verlag)
978-3-642-43218-7 (ISBN)
CHF 269,60 inkl. MwSt
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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.

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.

Introduction.- Supervised Sequence Labelling.- Neural Networks.- Long Short-Term Memory.- A Comparison of Network Architectures.- Hidden Markov Model Hybrids.- Connectionist Temporal Classification.- Multidimensional Networks.- Hierarchical Subsampling Networks.

Erscheint lt. Verlag 13.4.2014
Reihe/Serie Studies in Computational Intelligence
Zusatzinfo XIV, 146 p.
Verlagsort Berlin
Sprache englisch
Maße 155 x 235 mm
Gewicht 253 g
Themenwelt Informatik Theorie / Studium Künstliche Intelligenz / Robotik
Technik
Schlagworte Computational Intelligence • Neural networks • Recurrent Neural Networks • Sequence Labelling
ISBN-10 3-642-43218-2 / 3642432182
ISBN-13 978-3-642-43218-7 / 9783642432187
Zustand Neuware
Informationen gemäß Produktsicherheitsverordnung (GPSR)
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