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Machine Learning Methods by Hang (33 results)
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hardcover. Condition: Good. Connecting readers with great books since 1972! Used textbooks may not include companion materials such as access codes, etc. May have some wear or writing/highlighting. We ship orders daily and Customer Service is our top priority!
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Condition: New.
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Machine Learning Methods
Language: English
Published by Springer Verlag, Singapore, 2023
ISBN 10: 981993916X ISBN 13: 9789819939169
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Machine Learning Methods
Language: English
Published by Springer Verlag, Singapore, 2023
ISBN 10: 981993916X ISBN 13: 9789819939169
Seller: PBShop.store UK, Fairford, GLOS, United Kingdom
Seller rating 5 out of 5 stars
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Condition: NEW.
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Condition: As New. Unread book in perfect condition.
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Machine Learning Methods
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Condition: New.
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Hardcover. Condition: New.
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Machine Learning Methods
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
£ 85.36
£ 11.98 shipping
Ships from United Kingdom to U.S.A.Quantity: Over 20 available
Add to basketCondition: New. In English.
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Condition: New.
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Machine Learning Methods
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: As New. Unread book in perfect condition.
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Condition: NEW.
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Machine Learning Methods
Language: English
Published by Springer Verlag, Singapore, SG, 2023
ISBN 10: 981993916X ISBN 13: 9789819939169
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. 2024 ed.
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Taschenbuch. Condition: Neu. Machine Learning Methods | Hang Li | Taschenbuch | xv | Englisch | 2024 | Springer | EAN 9789819939190 | Verantwortliche Person für die EU: Springer Verlag GmbH, Tiergartenstr. 17, 69121 Heidelberg, juergen[dot]hartmann[at]springer[dot]com | Anbieter: preigu.
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Condition: Sehr gut. Zustand: Sehr gut | Seiten: 547 | Sprache: Englisch | Produktart: Bücher | This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.
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Condition: New.
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Machine Learning Methods
Language: English
Published by Springer Nature Singapore, 2023
ISBN 10: 981993916X ISBN 13: 9789819939169
Condition: New. Provides introduction to principle machine learning methods, covering both supervised and unsupervised learning methodsPresents clear descriptions, detailed proofs, and concrete examples using concise languageWritten by a leading expert on .
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Hardcover. Condition: Brand New. 547 pages. 9.25x6.10x1.38 inches. In Stock.
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Buch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.
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Machine Learning Methods
Language: English
Published by Springer Verlag, Singapore, SG, 2023
ISBN 10: 981993916X ISBN 13: 9789819939169
Hardback. Condition: New. 2024 ed.
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Machine Learning Methods
Language: English
Published by Springer Verlag Gmbh Dez 2024, 2024
ISBN 10: 9819939194 ISBN 13: 9789819939190
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware Englisch.
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Machine Learning Methods
Language: English
Published by Springer Verlag GmbH, 2024
ISBN 10: 9819939194 ISBN 13: 9789819939190
Condition: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt.
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Condition: New. Print on Demand.
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Machine Learning Methods
Language: English
Published by Singapore Springer Verlag Okt 2023, 2023
ISBN 10: 981993916X ISBN 13: 9789819939169
Seller: BuchWeltWeit Ludwig Meier e.K., Bergisch Gladbach, Germany
Seller rating 5 out of 5 stars
Buch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning. 532 pp. Englisch.
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Condition: New. PRINT ON DEMAND.
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Hardcover. Condition: Brand New. 547 pages. 9.25x6.10x1.38 inches. In Stock. This item is printed on demand.
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Machine Learning Methods
Language: English
Published by Springer, Springer Dez 2024, 2024
ISBN 10: 9819939194 ISBN 13: 9789819939190
Seller: buchversandmimpf2000, Emtmannsberg, BAYE, Germany
Seller rating 5 out of 5 stars
Taschenbuch. Condition: Neu. This item is printed on demand - Print on Demand Titel. Neuware -This book provides a comprehensive and systematic introduction to the principal machine learning methods, covering both supervised and unsupervised learning methods. It discusses essential methods of classification and regression in supervised learning, such as decision trees, perceptrons, support vector machines, maximum entropy models, logistic regression models and multiclass classification, as well as methods applied in supervised learning, like the hidden Markov model and conditional random fields. In the context of unsupervised learning, it examines clustering and other problems as well as methods such as singular value decomposition, principal component analysis and latent semantic analysis. As a fundamental book on machine learning, it addresses the needs of researchers and students who apply machine learning as an important tool in their research, especially those in fields such as information retrieval, natural language processing and text data mining. In order to understand the concepts and methods discussed, readers are expected to have an elementary knowledge of advanced mathematics, linear algebra and probability statistics. The detailed explanations of basic principles, underlying concepts and algorithms enable readers to grasp basic techniques, while the rigorous mathematical derivations and specific examples included offer valuable insights into machine learning.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 548 pp. Englisch.
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Condition: New. Print on Demand.
