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Lifelong machine learning / (Record no. 562230)

000 -LEADER
fixed length control field 07599nam a2200661 i 4500
001 - CONTROL NUMBER
control field 7748636
003 - CONTROL NUMBER IDENTIFIER
control field IEEE
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20200413152922.0
006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS
fixed length control field m eo d
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION
fixed length control field cr cn |||m|||a
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 161205s2017 cau foab 000 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9781627058773
Qualifying information ebook
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
Canceled/invalid ISBN 9781627055017
Qualifying information print
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code 10.2200/S00737ED1V01Y201610AIM033
Source of number or code doi
035 ## - SYSTEM CONTROL NUMBER
System control number (CaBNVSL)swl00406949
035 ## - SYSTEM CONTROL NUMBER
System control number (OCoLC)962492936
040 ## - CATALOGING SOURCE
Original cataloging agency CaBNVSL
Language of cataloging eng
Description conventions rda
Transcribing agency CaBNVSL
Modifying agency CaBNVSL
050 #4 - LIBRARY OF CONGRESS CALL NUMBER
Classification number Q325.5
Item number .C445 2017
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER
Classification number 006.31
Edition number 23
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Chen, Zhiyuan
Titles and words associated with a name (Computer scientist),
Relator term author.
245 10 - TITLE STATEMENT
Title Lifelong machine learning /
Statement of responsibility, etc. Zhiyuan Chen, Bing Liu.
264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE
Place of production, publication, distribution, manufacture [San Rafael, California] :
Name of producer, publisher, distributor, manufacturer Morgan & Claypool,
Date of production, publication, distribution, manufacture, or copyright notice 2017.
300 ## - PHYSICAL DESCRIPTION
Extent 1 PDF (xvii, 127 pages)
336 ## - CONTENT TYPE
Content type term text
Source rdacontent
337 ## - MEDIA TYPE
Media type term electronic
Source isbdmedia
338 ## - CARRIER TYPE
Carrier type term online resource
Source rdacarrier
490 1# - SERIES STATEMENT
Series statement Synthesis lectures on artificial intelligence and machine learning,
International Standard Serial Number 1939-4616 ;
Volume/sequential designation # 33
538 ## - SYSTEM DETAILS NOTE
System details note Mode of access: World Wide Web.
538 ## - SYSTEM DETAILS NOTE
System details note System requirements: Adobe Acrobat Reader.
500 ## - GENERAL NOTE
General note Part of: Synthesis digital library of engineering and computer science.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc. note Includes bibliographical references (pages 111-125).
505 0# - FORMATTED CONTENTS NOTE
Formatted contents note 1. Introduction -- 1.1 A brief history of lifelong learning -- 1.2 Definition of lifelong learning -- 1.3 Lifelong learning system architecture -- 1.4 Evaluation methodology -- 1.5 Role of big data in lifelong learning -- 1.6 Outline of the book --
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 2. Related learning paradigms -- 2.1 Transfer learning -- 2.1.1 Structural correspondence learning -- 2.1.2 Naïve Bayes transfer classifier -- 2.1.3 Deep learning in transfer learning -- 2.1.4 Difference from lifelong learning -- 2.2 Multi-task learning -- 2.2.1 Task relatedness in multi-task learning -- 2.2.2 GO-MTL: multi-task learning using latent basis -- 2.2.3 Deep learning in multi-task learning -- 2.2.4 Difference from lifelong learning -- 2.3 Online learning -- 2.3.1 Difference from lifelong learning -- 2.4 Reinforcement learning -- 2.4.1 Difference from lifelong learning -- 2.5 Summary --
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 3. Lifelong supervised learning -- 3.1 Definition and overview -- 3.2 Lifelong memory-based learning -- 3.2.1 Two memory-based learning methods -- 3.2.2 Learning a new representation for lifelong learning -- 3.3 Lifelong neural networks -- 3.3.1 MTL Net -- 3.3.2 Lifelong EBNN -- 3.4 Cumulative learning and self-motivated learning -- 3.4.1 Training a cumulative learning model -- 3.4.2 Testing a cumulative learning model -- 3.4.3 Open world learning for unseen class detection -- 3.5 ELLA: an efficient lifelong learning algorithm -- 3.5.1 Problem setting -- 3.5.2 Objective function -- 3.5.3 Dealing with the first inefficiency -- 3.5.4 Dealing with the second inefficiency -- 3.5.5 Active task selection -- 3.6 LSC: lifelong sentiment classification -- 3.6.1 Naïve Bayesian text classification -- 3.6.2 Basic ideas of LSC -- 3.6.3 LSC technique -- 3.7 Summary and evaluation datasets --
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 4. Lifelong unsupervised learning -- 4.1 Lifelong topic modeling -- 4.2 LTM: a lifelong topic model -- 4.2.1 LTM model -- 4.2.2 Topic knowledge mining -- 4.2.3 Incorporating past knowledge -- 4.2.4 Conditional distribution of Gibbs sampler -- 4.3 AMC: a lifelong topic model for small data -- 4.3.1 Overall algorithm of AMC -- 4.3.2 Mining must-link knowledge -- 4.3.3 Mining cannot-link knowledge -- 4.3.4 Extended Pólya Urn model -- 4.3.5 Sampling distributions in Gibbs sampler -- 4.4 Lifelong information extraction -- 4.4.1 Lifelong learning through recommendation -- 4.4.2 AER algorithm -- 4.4.3 Knowledge learning -- 4.4.4 Recommendation using past knowledge -- 4.5 Lifelong-RL: lifelong relaxation labeling -- 4.5.1 Relaxation labeling -- 4.5.2 Lifelong relaxation labeling -- 4.6 Summary and evaluation datasets --
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 5. Lifelong semi-supervised learning for information extraction -- 5.1 NELL: a never ending language learner -- 5.2 NELL architecture -- 5.3 Extractors and learning in NELL -- 5.4 Coupling constraints in NELL -- 5.5 Summary --
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 6. Lifelong reinforcement learning -- 6.1 Lifelong reinforcement learning through multiple environments -- 6.1.1 Acquiring and incorporating bias -- 6.2 Hierarchical Bayesian lifelong reinforcement learning -- 6.2.1 Motivation -- 6.2.2 Hierarchical Bayesian approach -- 6.2.3 MTRL algorithm -- 6.2.4 Updating hierarchical model parameters -- 6.2.5 Sampling an MDP -- 6.3 PG-ELLA: lifelong policy gradient reinforcement learning -- 6.3.1 Policy gradient reinforcement learning -- 6.3.2 Policy gradient lifelong learning setting -- 6.3.3 Objective function and optimization -- 6.3.4 Safe policy search for lifelong learning -- 6.3.5 Cross-domain lifelong reinforcement learning -- 6.4 Summary and evaluation datasets --
505 8# - FORMATTED CONTENTS NOTE
Formatted contents note 7. Conclusion and future directions -- Bibliography -- Authors' biographies.
506 1# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Abstract freely available; full-text restricted to subscribers or individual document purchasers.
510 0# - CITATION/REFERENCES NOTE
Name of source Compendex
510 0# - CITATION/REFERENCES NOTE
Name of source INSPEC
510 0# - CITATION/REFERENCES NOTE
Name of source Google scholar
510 0# - CITATION/REFERENCES NOTE
Name of source Google book search
520 3# - SUMMARY, ETC.
Summary, etc. Lifelong Machine Learning (or Lifelong Learning) is an advanced machine learning paradigm that learns continuously, accumulates the knowledge learned in previous tasks, and uses it to help future learning. In the process, the learner becomes more and more knowledgeable and effective at learning. This learning ability is one of the hallmarks of human intelligence. However, the current dominant machine learning paradigm learns in isolation: given a training dataset, it runs a machine learning algorithm on the dataset to produce a model. It makes no attempt to retain the learned knowledge and use it in future learning. Although this isolated learning paradigm has been very successful, it requires a large number of training examples, and is only suitable for well-defined and narrow tasks. In comparison, we humans can learn effectively with a few examples because we have accumulated so much knowledge in the past which enables us to learn with little data or effort. Lifelong learning aims to achieve this capability. As statistical machine learning matures, it is time to make a major effort to break the isolated learning tradition and to study lifelong learning to bring machine learning to new heights. Applications such as intelligent assistants, chatbots, and physical robots that interact with humans and systems in real-life environments are also calling for such lifelong learning capabilities. Without the ability to accumulate the learned knowledge and use it to learn more knowledge incrementally, a system will probably never be truly intelligent. This book serves as an introductory text and survey to lifelong learning.
530 ## - ADDITIONAL PHYSICAL FORM AVAILABLE NOTE
Additional physical form available note Also available in print.
588 ## - SOURCE OF DESCRIPTION NOTE
Source of description note Title from PDF title page (viewed on December 5, 2016).
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Machine learning.
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term lifelong machine learning
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term lifelong learning; learning with memory
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term cumulative learning
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term multi-task learning
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term transfer learning
700 1# - ADDED ENTRY--PERSONAL NAME
Personal name Liu, Bing,
Dates associated with a name 1963-,
Relator term author.
776 08 - ADDITIONAL PHYSICAL FORM ENTRY
Relationship information Print version:
International Standard Book Number 9781627055017
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Synthesis digital library of engineering and computer science.
830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE
Uniform title Synthesis lectures on artificial intelligence and machine learning ;
Volume/sequential designation # 33.
International Standard Serial Number 1939-4616
856 42 - ELECTRONIC LOCATION AND ACCESS
Materials specified Abstract with links to resource
Uniform Resource Identifier http://ieeexplore.ieee.org/servlet/opac?bknumber=7748636
Holdings
Withdrawn status Lost status Damaged status Not for loan Permanent Location Current Location Date acquired Barcode Date last seen Price effective from Koha item type
        PK Kelkar Library, IIT Kanpur PK Kelkar Library, IIT Kanpur 2020-04-13 EBKE730 2020-04-13 2020-04-13 E books

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