Articles liés à Feature Engineering for Machine Learning and Data Analytics
- ÉditeurCRC Press
- Date d'édition2018
- ISBN 10 1138744387
- ISBN 13 9781138744387
- ReliureRelié
- Langueanglais
- Nombre de pages400
- ÉditeurDong Guozhu, Liu Huan
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Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
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CRC Press, 2018
ISBN 10 : 1138744387
ISBN 13 : 9781138744387
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Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
Edité par
CRC Press, 2018
ISBN 10 : 1138744387
ISBN 13 : 9781138744387
Ancien ou d'occasion
Couverture rigide
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Feature Engineering for Machine Learning and Data Analytics
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CRC Press, 2018
ISBN 10 : 1138744387
ISBN 13 : 9781138744387
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Feature Engineering for Machine Learning and Data Analytics
Edité par
CRC Press, 2018
ISBN 10 : 1138744387
ISBN 13 : 9781138744387
Ancien ou d'occasion
Couverture rigide
Vendeur : GreatBookPrices, Columbia, MD, Etats-Unis
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Feature Engineering for Machine Learning and Data Analytics (Chapman & Hall/CRC Data Mining and Knowledge Discovery Series)
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CRC Press 2018-04-10, 2018
ISBN 10 : 1138744387
ISBN 13 : 9781138744387
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Feature Engineering for Machine Learning and Data Analytics
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Feature Engineering for Machine Learning and Data Analytics (Hardcover)
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Taylor & Francis Ltd, London, 2018
ISBN 10 : 1138744387
ISBN 13 : 9781138744387
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Hardcover. Etat : new. Hardcover. Feature engineering plays a vital role in big data analytics. Machine learning and data mining algorithms cannot work without data. Little can be achieved if there are few features to represent the underlying data objects, and the quality of results of those algorithms largely depends on the quality of the available features. Feature Engineering for Machine Learning and Data Analytics provides a comprehensive introduction to feature engineering, including feature generation, feature extraction, feature transformation, feature selection, and feature analysis and evaluation. The book presents key concepts, methods, examples, and applications, as well as chapters on feature engineering for major data types such as texts, images, sequences, time series, graphs, streaming data, software engineering data, Twitter data, and social media data. It also contains generic feature generation approaches, as well as methods for generating tried-and-tested, hand-crafted, domain-specific features.The first chapter defines the concepts of features and feature engineering, offers an overview of the book, and provides pointers to topics not covered in this book. The next six chapters are devoted to feature engineering, including feature generation for specific data types. The subsequent four chapters cover generic approaches for feature engineering, namely feature selection, feature transformation based feature engineering, deep learning based feature engineering, and pattern based feature generation and engineering. The last three chapters discuss feature engineering for social bot detection, software management, and Twitter-based applications respectively.This book can be used as a reference for data analysts, big data scientists, data preprocessing workers, project managers, project developers, prediction modelers, professors, researchers, graduate students, and upper level undergraduate students. It can also be used as the primary text for courses on feature engineering, or as a supplement for courses on machine learning, data mining, and big data analytics. Edited by two of the leading experts in the field, this book provides a comprehensive reference book on feature engineering. The book provides a description of problems and applications for feature engineering, as well as its techniques, principles, issues, and challenges. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. N° de réf. du vendeur 9781138744387
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