Synthesis Lectures on Artificial Intelligence and Machine Learning- Lifelong Machine Learning Second Edition
Afbeeldingen
Sla de afbeeldingen overArtikel vergelijken
Auteur:
Zhiyuan Chen
Bing Liu
- Engels
- Paperback
- 9781681733029
- 30 augustus 2018
- 207 pagina's
Samenvatting
Research in lifelong learning has developed significantly in the relatively short time since the first edition of this book was published. This second edition expands the definition of lifelong learning, updates the content, and adds a new chapter on continual learning in deep neural networks. A few chapters have also been reorganised.
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.
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.
Productspecificaties
Wij vonden geen specificaties voor jouw zoekopdracht '{SEARCH}'.
Inhoud
- Taal
- en
- Bindwijze
- Paperback
- Oorspronkelijke releasedatum
- 30 augustus 2018
- Aantal pagina's
- 207
- Illustraties
- Nee
Betrokkenen
- Hoofdauteur
- Zhiyuan Chen
- Tweede Auteur
- Bing Liu
- Hoofdredacteur
- Ronald Brachman
- Hoofduitgeverij
- Morgan & Claypool Publishers
Overige kenmerken
- Editie
- 2
- Extra groot lettertype
- Nee
- Product breedte
- 191 mm
- Product lengte
- 235 mm
- Studieboek
- Ja
- Verpakking breedte
- 191 mm
- Verpakking hoogte
- 235 mm
- Verpakking lengte
- 235 mm
- Verpakkingsgewicht
- 410 g
EAN
- EAN
- 9781681733029
Je vindt dit artikel in
- Categorieën
- Taal
- Engels
- Boek, ebook of luisterboek?
- Boek
- Beschikbaarheid
- Leverbaar
- Studieboek of algemeen
- Studieboeken
Kies gewenste uitvoering
Kies je bindwijze
(2)
Prijsinformatie en bestellen
De prijs van dit product is 104 euro en 99 cent.
2 - 3 weken
Verkoop door bol
- Prijs inclusief verzendkosten, verstuurd door bol
- Ophalen bij een bol afhaalpunt mogelijk
- 30 dagen bedenktijd en gratis retourneren
- Dag en nacht klantenservice
Rapporteer dit artikel
Je wilt melding doen van illegale inhoud over dit artikel:
- Ik wil melding doen als klant
- Ik wil melding doen als autoriteit of trusted flagger
- Ik wil melding doen als partner
- Ik wil melding doen als merkhouder
Geen klant, autoriteit, trusted flagger, merkhouder of partner? Gebruik dan onderstaande link om melding te doen.