Supervised and Unsupervised Learning for Data Science

Name: Supervised and Unsupervised Learning for Data Science
Brand: Springer Nature Switzerland AG
SKU: 9783030224776
Price: 553.38 RON
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Autor: Bee Wah Yap, Azlinah Mohamed

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Editura: Springer Nature Switzerland AG

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This book covers the state of the art in learning algorithms with an inclusion of semi-supervised me...

Informații despre carte

Autor

Bee Wah Yap, Azlinah Mohamed

Limbă

engleză

Legare

Carte - Carte broșată

Publicat

2020

Pagini

187

EAN

9783030224776

ISBN

3030224775

Enbook ID

33231067

Editura

Springer Nature Switzerland AG

Greutate

308

Dimensiuni

155 x 235 x 11

Descriere completă

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, classification, clustering, topic modeling, graph analysis 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).Includes new advances in clustering and classification using semi-supervised and unsupervised learning;Address new challenges arising in feature extraction and selection using semi-supervised and unsupervised learning;Features applications from healthcare, engineering, and text/social media mining that exploit techniques from semi-supervised and unsupervised learning.

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