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.
| ISBN: | 9783030224776 |
| Publication date: | 19th September 2020 |
| Author: | Michael W Berry |
| Publisher: | Springer Nature Switzerland AG |
| Format: | Paperback |
| Pagination: | 187 pages |
| Series: | Unsupervised and Semi-Supervised Learning |
| Genres: |
Communications engineering / telecommunications Electronics engineering Digital signal processing (DSP) Pattern recognition Artificial intelligence Data mining Expert systems / knowledge-based systems |
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.
Supervised and Unsupervised Learning for Data Science features in the following genres: Communications engineering / telecommunications, Electronics engineering, Digital signal processing (DSP), Pattern recognition, Artificial intelligence, Data mining, Expert systems / knowledge-based systems
Supervised and Unsupervised Learning for Data Science is available in Paperback
Supervised and Unsupervised Learning for Data Science was written by Michael W Berry and published by Springer Nature Switzerland AG
Supervised and Unsupervised Learning for Data Science has 187 pages
Yes it is part of Unsupervised and Semi-Supervised Learning series
£80.99