Wang wenmin (34 résultats)

Hardback. Etat : Good. Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples. The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled ?Perspectives,? comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, ?Frameworks?: subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, ?Paradigms,? encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, ?Tasks?: comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction. This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning. The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.…

hardcover. Etat : Very Good.

hardcover. Etat : Good. 2024th Edition. Ships in a BOX from Central Missouri! May not include working access code. Will not include dust jacket. Has used sticker(s) and some writing or highlighting. UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

hardcover. Etat : LikeNew. Used Like New, no missing pages, no damage to binding, may have a remainder mark.

hardcover. Etat : New. 2024th Edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).

Etat : New.

Etat : New. This is a Brand-new US Edition. This Item may be shipped from US or any other country as we have multiple locations worldwide.

Etat : Brand New. New. US edition. Expediting shipping for all USA and Europe orders excluding PO Box. Excellent Customer Service.

Etat : New. Brand New Original US Edition. Customer service! Satisfaction Guaranteed.

Etat : As New. Unread book in perfect condition.

Etat : New. 2024th edition NO-PA16APR2015-KAP.

Etat : New.

Hardcover. Etat : new. New Copy. Customer Service Guaranteed.

Etat : New.

Etat : New.

Etat : As New. Unread book in perfect condition.

Etat : New. In English.

Hardback. Etat : New. 2024 ed. Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled "Perspectives," comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, "Frameworks": subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, "Paradigms," encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, "Tasks": comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.…

Taschenbuch. Etat : Neu. Principles of Machine Learning | The Three Perspectives | Wenmin Wang | Taschenbuch | xxxv | Englisch | 2025 | Springer | EAN 9789819753352 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu. …

Buch. Etat : Neu. Druck auf Anfrage Neuware - Printed after ordering - Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled 'Perspectives,' comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, 'Frameworks': subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, 'Paradigms,' encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, 'Tasks': comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.…

Hardcover. Etat : Brand New. 500 pages. 9.25x6.10x9.25 inches. In Stock.

Hardback. Etat : New. 2024 ed. Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled "Perspectives," comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, "Frameworks": subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, "Paradigms," encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, "Tasks": comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.…

Hardcover. Etat : New.

Taschenbuch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled 'Perspectives,' comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, 'Frameworks': subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, 'Paradigms,' encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, 'Tasks': comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content. 564 pp. Englisch.…

Hardcover. Etat : new. Hardcover. Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled Perspectives, comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, Frameworks: subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, Paradigms, encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, Tasks: comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content. Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Taschenbuch. Etat : Neu. nach der Bestellung gedruckt Neuware - Printed after ordering - Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled 'Perspectives,' comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, 'Frameworks': subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, 'Paradigms,' encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, 'Tasks': comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.…

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.

Buch. Etat : Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled 'Perspectives,' comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, 'Frameworks': subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, 'Paradigms,' encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, 'Tasks': comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content. 529 pp. Englisch. …

Etat : New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the.…

Taschenbuch. Etat : Neu. This item is printed on demand - Print on Demand Titel. Neuware -Conducting an in-depth analysis of machine learning, this book proposes three perspectives for studying machine learning: the learning frameworks, learning paradigms, and learning tasks. With this categorization, the learning frameworks reside within the theoretical perspective, the learning paradigms pertain to the methodological perspective, and the learning tasks are situated within the problematic perspective. Throughout the book, a systematic explication of machine learning principles from these three perspectives is provided, interspersed with some examples.The book is structured into four parts, encompassing a total of fifteen chapters. The inaugural part, titled 'Perspectives,' comprises two chapters: an introductory exposition and an exploration of the conceptual foundations. The second part, 'Frameworks': subdivided into five chapters, each dedicated to the discussion of five seminal frameworks: probability, statistics, connectionism, symbolism, and behaviorism. Continuing further, the third part, 'Paradigms,' encompasses four chapters that explain the three paradigms of supervised learning, unsupervised learning, and reinforcement learning, and narrating several quasi-paradigms emerged in machine learning. Finally, the fourth part, 'Tasks': comprises four chapters, delving into the prevalent learning tasks of classification, regression, clustering, and dimensionality reduction.This book provides a multi-dimensional and systematic interpretation of machine learning, rendering it suitable as a textbook reference for senior undergraduates or graduate students pursuing studies in artificial intelligence, machine learning, data science, computer science, and related disciplines. Additionally, it serves as a valuable reference for those engaged in scientific research and technical endeavors within the realm of machine learning.The translation was done with the help of artificial intelligence. A subsequent human revision was done primarily in terms of content.Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg 564 pp. Englisch.…