Supervised Sequence Labelling with Recurrent Neural Networks. 1st ed. 2012

種類:

電子ブック

責任表示:

by Alex Graves

出版情報:

Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2012

著者名:

• Graves, Alex.
• SpringerLink (Online service)

シリーズ名:

Studies in Computational Intelligence ; 385

ISBN:

9783642247972 [3642247970]    

注記:

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.
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 recurr

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