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Supervised and Unsupervised Learning for Data Science

Produktform: Buch / Einband - fest (Hardcover)

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a comprehensive reference to the field. The book is organized into eight chapters that cover the following topics: discretization, feature extraction and selection, association rule learning, classification, clustering, anomaly detection, and applications. Practitioners and graduate students can use the volume as an important reference for their current and future research and faculty will find the volume useful for assignments in presenting current approaches to unsupervised and semi-supervised learning in graduate-level seminar courses. The book is based on selected, expanded papers from the Fourth International Conference on Soft Computing in Data Science (2018).  weiterlesen

Dieser Artikel gehört zu den folgenden Serien

Sprache(n): Englisch

ISBN: 978-3-030-22474-5 / 978-3030224745 / 9783030224745

Verlag: Springer International Publishing

Erscheinungsdatum: 19.09.2019

Seiten: 187

Auflage: 1

Herausgegeben von Michael W. Berry, Bee Wah Yap, Azlinah Mohamed

106,99 € inkl. MwSt.
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lieferbar - Lieferzeit 10-15 Werktage

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