| 书目名称 | Supervised and Unsupervised Learning for Data Science |
| 编辑 | Michael W. Berry,Azlinah Mohamed,Bee Wah Yap |
| 视频video | |
| 概述 | 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 unsu |
| 丛书名称 | Unsupervised and Semi-Supervised Learning |
| 图书封面 |  |
| 描述 | .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 |
| 出版日期 | Book 2020 |
| 关键词 | Semi-supervised and unsupervised learning; Feature extraction and selection; Discretization; Learning D |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-030-22475-2 |
| isbn_softcover | 978-3-030-22477-6 |
| isbn_ebook | 978-3-030-22475-2Series ISSN 2522-848X Series E-ISSN 2522-8498 |
| issn_series | 2522-848X |
| copyright | Springer Nature Switzerland AG 2020 |
发表于 2025-3-21 17:50:37
