國立高雄大學圖資館 |

語系: 繁體中文

說明(常見問題)

圖資館首頁

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Lifelong machine learning

Brachman, Ronald,

Lifelong machine learning

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Lifelong machine learningZhiyuan Chen, Bing Liu.
作者:	Chen, Zhiyuan.
其他作者:	Liu, Bing,
出版者:	San Rafael, California :Morgan & Claypool Publishers,2018.
面頁冊數:	1 online resource (209 p.)
標題:	Machine learning.
電子資源:	click for full text
ISBN:	1681733021

Lifelong machine learning
Chen, Zhiyuan.

Lifelong machine learning[electronic resource] /Zhiyuan Chen, Bing Liu. - 2nd ed. - San Rafael, California :Morgan & Claypool Publishers,2018. - 1 online resource (209 p.) - Synthesis Lectures on Artificial Intelligence and Machine Learning.. - Synthesis Lectures on Artificial Intelligence and Machine Learning..

Includes bibliographical references and index.

Lifelong machine learning, second edition -- Synthesis Lectures on Artificial Intelligence and Machine Learning -- Abstract -- Dedication -- Contents -- Preface -- Acknowledgments -- Chapter 1. Introduction -- Chapter 2. Related Learning Paradigms -- Chapter 3. Lifelong Supervised Learning -- Chapter 4. Continual Learning and Catastrophic Forgetting -- Chapter 5. Open-World Learning -- Chapter 6. Lifelong Topic Modeling -- Chapter 7. Lifelong Information Extraction -- Chapter 8. Continuous Knowledge Learning in Chatbots -- Chapter 9. Lifelong Reinforcement Learning -- Chapter 10. Conclusion and Future Directions -- Bibliography -- Authors' Biographies.

Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, 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 that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks-which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning-most notably, multi-task learning, transfer learning, and meta-learning-because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.

ISBN: 1681733021Subjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325

Dewey Class. No.: 006

Lifelong machine learning
LDR:03837nmm a2200301 i 4500 001 559482
006 m o d
007 cr cn|||||||||
008 191226s2018 cau ob 000 0 eng d
020 $a 1681733021
020 $a 168173303X
020 $a 1681733048
020 $a 9781681733029
020 $a 9781681733036
020 $a 9781681733043
035 $a MCPB0006417
040 $a iG Publishing $b eng $e aacr2 $c iG Publishing
041 0 $a eng
050 0 0 $a Q325
082 0 4 $a 006
100 1 $a Chen, Zhiyuan. $3 842629
245 1 0 $a Lifelong machine learning $h [electronic resource] / $c Zhiyuan Chen, Bing Liu.
250 $a 2nd ed.
260 $a San Rafael, California : $b Morgan & Claypool Publishers, $c 2018.
300 $a 1 online resource (209 p.)
490 1 $a Synthesis Lectures on Artificial Intelligence and Machine Learning.
504 $a Includes bibliographical references and index.
505 0 $a Lifelong machine learning, second edition -- Synthesis Lectures on Artificial Intelligence and Machine Learning -- Abstract -- Dedication -- Contents -- Preface -- Acknowledgments -- Chapter 1. Introduction -- Chapter 2. Related Learning Paradigms -- Chapter 3. Lifelong Supervised Learning -- Chapter 4. Continual Learning and Catastrophic Forgetting -- Chapter 5. Open-World Learning -- Chapter 6. Lifelong Topic Modeling -- Chapter 7. Lifelong Information Extraction -- Chapter 8. Continuous Knowledge Learning in Chatbots -- Chapter 9. Lifelong Reinforcement Learning -- Chapter 10. Conclusion and Future Directions -- Bibliography -- Authors' Biographies.
520 3 $a Lifelong Machine Learning, Second Edition is an introduction to an advanced machine learning paradigm that continuously learns by accumulating past knowledge that it then uses in future learning and problem solving. In contrast, 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 that is then used in its intended application. It makes no attempt to retain the learned knowledge and use it in subsequent learning. Unlike this isolated system, humans learn effectively with only a few examples precisely because our learning is very knowledge-driven: the knowledge learned in the past helps us learn new things with little data or effort. Lifelong learning aims to emulate this capability, because without it, an AI system cannot be considered truly intelligent. Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. The purpose of this second edition is to expand the definition of lifelong learning, update the content of several chapters, and add a new chapter about continual learning in deep neural networks-which has been actively researched over the past two or three years. A few chapters have also been reorganized to make each of them more coherent for the reader. Moreover, the authors want to propose a unified framework for the research area. Currently, there are several research topics in machine learning that are closely related to lifelong learning-most notably, multi-task learning, transfer learning, and meta-learning-because they also employ the idea of knowledge sharing and transfer. This book brings all these topics under one roof and discusses their similarities and differences. Its goal is to introduce this emerging machine learning paradigm and present a comprehensive survey and review of the important research results and latest ideas in the area. This book is thus suitable for students, researchers, and practitioners who are interested in machine learning, data mining, natural language processing, or pattern recognition. Lecturers can readily use the book for courses in any of these related fields.
650 0 $a Machine learning. $3 188639
650 0 $a Computer Science. $3 212513
650 0 $a Neural Networks. $3 842633
700 1 $a Liu, Bing, $e editor. $3 783684
700 1 $a Brachman, Ronald, $e editor. $3 842630
700 1 $a Stone, Peter, $e editor. $3 842631
830 0 $a Synthesis Lectures on Artificial Intelligence and Machine Learning. $3 842632
856 4 0 $u http://portal.igpublish.com/iglibrary/search/MCPB0006417.html $z click for full text