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Machine Learning Principles and Applications(Chinese Edition)
Published by Machinery Industry Press, 2021
ISBN 10: 7111682947 ISBN 13: 9787111682943
Language: English
US$ 96.08
Convert currencyUS$ 18.00 shipping from China to U.S.A.Quantity: 1 available
Add to basketpaperback. Condition: New. Paperback.Pub Date:2021-09-01 Pages:212 Language:Chinese Publisher:Machinery Industry Press Machine Learning Principles and Applications focuses on machine learning and its algorithms. and introduces its theoretical details and application methods in detail. Machine Learning Principles and Applications has 19 chapters. which introduce the overview of machine learning. linear regression and maximum entropy model. k-nearest neighbor algorithm. decision tree model. naive Bayes classifier. suppor.
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Machine Learning Principles and Applications(Chinese Edition)
Published by Machinery Industry Press, 2021
ISBN 10: 7111682947 ISBN 13: 9787111682943
Language: Chinese
US$ 96.08
Convert currencyUS$ 18.00 shipping from China to U.S.A.Quantity: 3 available
Add to basketpaperback. Condition: New. Paperback.Pub Date:2021-09-01 Pages:212 Language:Chinese Publisher:Machinery Industry Press Machine Learning Principles and Applications focuses on machine learning and its algorithms. and introduces its theoretical details and application methods in detail. Machine Learning Principles and Applications has 19 chapters. which introduce the overview of machine learning. linear regression and maximum entropy model. k-nearest neighbor algorithm. decision tree model. naive Bayes classifier. suppor.
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US$ 96.82
Convert currencyUS$ 18.00 shipping from China to U.S.A.Quantity: 3 available
Add to basketpaperback. Condition: New. Language:Chinese.Paperback. Pub Date: 2021-05-01 Pages: 228 Publisher: Tsinghua University Press This book takes machine learning algorithms as the subject. and introduces the theoretical details and application methods of algorithms in detail.?The book consists of 19 chapters. which respectively introduce logistic regression and z large entropy model. k-nearest neighbor model. decision tree model. naive Bayes model. support vector machine model. integrated learning framework. EM algorithm. d.
