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Machine Learning by Zhou Zhi Hua (94 results)
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Ensemble Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Published by Chapman and Hall/CRC, 2012
ISBN 10: 1439830037 ISBN 13: 9781439830031
Language: English
First Edition
hardcover. Condition: Good. 1st 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).
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Machine Learning
Seller: Bay State Book Company, North Smithfield, RI, U.S.A.
Seller rating 5 out of 5 stars
Condition: good. The book is in good condition with all pages and cover intact, including the dust jacket if originally issued. The spine may show light wear. Pages may contain some notes or highlighting, and there might be a "From the library of" label. Boxed set packaging, shrink wrap, or included media like CDs may be missing.
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Machine Learning
Seller: Bay State Book Company, North Smithfield, RI, U.S.A.
Seller rating 5 out of 5 stars
Condition: good. The book is in good condition with all pages and cover intact, including the dust jacket if originally issued. The spine may show light wear. Pages may contain some notes or highlighting, and there might be a "From the library of" label. Boxed set packaging, shrink wrap, or included media like CDs may be missing.
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Ensemble Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Published by Chapman and Hall/CRC, 2012
ISBN 10: 1439830037 ISBN 13: 9781439830031
Language: English
hardcover. Condition: New.
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Ensemble Methods (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
Published by Chapman and Hall/CRC, 2012
ISBN 10: 1439830037 ISBN 13: 9781439830031
Language: English
First Edition
hardcover. Condition: New. 1st Edition. Ships in a BOX from Central Missouri! UPS shipping for most packages, (Priority Mail for AK/HI/APO/PO Boxes).
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Condition: As New. Unread book in perfect condition.
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Condition: New.
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Machine Learning
Published by Springer Verlag, Singapore, 2021
ISBN 10: 9811519668 ISBN 13: 9789811519666
Language: English
HRD. Condition: New. New Book. Shipped from UK. Established seller since 2000.
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Condition: New.
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Condition: New.
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Condition: New.
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Machine Learning
Published by Springer Verlag, Singapore, 2021
ISBN 10: 9811519668 ISBN 13: 9789811519666
Language: English
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: As New. Unread book in perfect condition.
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Condition: New.
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Advances in Machine Learning: First Asian Conference on Machine Learning, ACML 2009, Nanjing, China, November 2-4, 2009. Proceedings (Lecture Notes in Computer Science, 5828)
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In.
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Machine Learning
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In English.
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Condition: New.
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Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Hardcover. Condition: New.
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Condition: New. pp. 432.
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Machine Learning
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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Machine Learning
Seller: Ria Christie Collections, Uxbridge, United Kingdom
Seller rating 5 out of 5 stars
Condition: New. In English.
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Machine Learning
Published by Springer Verlag, Singapore, SG, 2021
ISBN 10: 9811519668 ISBN 13: 9789811519666
Language: English
Seller: Rarewaves.com USA, London, LONDO, United Kingdom
Seller rating 5 out of 5 stars
Hardback. Condition: New. 2021 ed. Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.
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PF. Condition: New.
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Hardcover. Condition: Brand New. 458 pages. 9.75x6.75x1.00 inches. In Stock.
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Machine Learning
Seller: GreatBookPricesUK, Woodford Green, United Kingdom
Seller rating 5 out of 5 stars
Condition: New.
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Machine Learning
Published by Springer Verlag, Singapore, SG, 2022
ISBN 10: 9811519692 ISBN 13: 9789811519697
Language: English
Paperback. Condition: New. 2021 ed. Machine Learning, a vital and core area of artificial intelligence (AI), is propelling the AI field ever further and making it one of the most compelling areas of computer science research. This textbook offers a comprehensive and unbiased introduction to almost all aspects of machine learning, from the fundamentals to advanced topics. It consists of 16 chapters divided into three parts: Part 1 (Chapters 1-3) introduces the fundamentals of machine learning, including terminology, basic principles, evaluation, and linear models; Part 2 (Chapters 4-10) presents classic and commonly used machine learning methods, such as decision trees, neural networks, support vector machines, Bayesian classifiers, ensemble methods, clustering, dimension reduction and metric learning; Part 3 (Chapters 11-16) introduces some advanced topics, covering feature selection and sparse learning, computational learning theory, semi-supervised learning, probabilistic graphical models, rule learning, and reinforcement learning. Each chapter includes exercises and further reading, so that readers can explore areas of interest. The book can be used as an undergraduate or postgraduate textbook for computer science, computer engineering, electrical engineering, data science, and related majors. It is also a useful reference resource for researchers and practitioners of machine learning.
