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Supervised Sequence Labelling with Recurrent Neural Networks (Record no. 11702)

000 -LEADER
fixed length control field 03048nam a22003975i 4500
003 - CONTROL NUMBER IDENTIFIER
control field OSt
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20140310143349.0
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr nn 008mamaa
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 120205s2012 gw | s |||| 0|eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9783642247972
978-3-642-24797-2
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.3
Edition number 23
264 #1 -
-- Berlin, Heidelberg :
-- Springer Berlin Heidelberg,
-- 2012.
912 ## -
-- ZDB-2-ENG
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Graves, Alex.
Relator term author.
245 10 - IMMEDIATE SOURCE OF ACQUISITION NOTE
Title Supervised Sequence Labelling with Recurrent Neural Networks
Medium [electronic resource] /
Statement of responsibility, etc by Alex Graves.
300 ## - PHYSICAL DESCRIPTION
Extent XIV, 146p. 50 illus., 12 illus. in color.
Other physical details online resource.
440 1# - SERIES STATEMENT/ADDED ENTRY--TITLE
Title Studies in Computational Intelligence,
International Standard Serial Number 1860-949X ;
Volume number/sequential designation 385
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 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.
520 ## - SUMMARY, ETC.
Summary, etc 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.
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Engineering.
Topical term or geographic name as entry element Artificial intelligence.
Topical term or geographic name as entry element Engineering.
Topical term or geographic name as entry element Computational Intelligence.
Topical term or geographic name as entry element Artificial Intelligence (incl. Robotics).
710 2# - ADDED ENTRY--CORPORATE NAME
Corporate name or jurisdiction name as entry element SpringerLink (Online service)
773 0# - HOST ITEM ENTRY
Title Springer eBooks
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Display text Printed edition:
International Standard Book Number 9783642247965
856 40 - ELECTRONIC LOCATION AND ACCESS
Uniform Resource Identifier http://dx.doi.org/10.1007/978-3-642-24797-2
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type E-Book
Copies
Price effective from Permanent location Date last seen Not for loan Date acquired Source of classification or shelving scheme Koha item type Damaged status Lost status Withdrawn status Current location Full call number
2014-04-02AUM Main Library2014-04-02 2014-04-02 E-Book   AUM Main Library006.3

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