Supervised and unsupervised learning for data science / Michael W. Berry, Azlinah Mohamed, Bee Wah Yap.
2020
Q325.5
Linked e-resources
Linked Resource
Online Access
Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Details
Title
Supervised and unsupervised learning for data science / Michael W. Berry, Azlinah Mohamed, Bee Wah Yap.
ISBN
9783030224752 (electronic book)
3030224759 (electronic book)
3030224740
9783030224745
3030224759 (electronic book)
3030224740
9783030224745
Publication Details
Cham : Springer, 2020.
Language
English
Description
1 online resource (191 pages)
Item Number
10.1007/978-3-030-22
Call Number
Q325.5
Dewey Decimal Classification
006.31
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Berry, Michael W.
Mohamed, Azlinah Hj.
Wah, Yap Bee.
Mohamed, Azlinah Hj.
Wah, Yap Bee.
Series
Unsupervised and semi-supervised learning.
Available in Other Form
Supervised and Unsupervised Learning for Data Science.
Linked Resources
Online Access
Record Appears in
Online Resources > Ebooks
All Resources
All Resources