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Supervised and unsupervised learning...

Berry, Michael W.

Supervised and unsupervised learning for data science

Record Type:	Electronic resources : Monograph/item
Title/Author:	Supervised and unsupervised learning for data scienceedited by Michael W. Berry, Azlinah Mohamed, Bee Wah Yap.
other author:	Berry, Michael W.
Published:	Cham :Springer International Publishing :2020.
Description:	viii, 187 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Machine learning.
Online resource:	https://doi.org/10.1007/978-3-030-22475-2
ISBN:	9783030224752$q(electronic bk.)

Supervised and unsupervised learning for data science
Supervised and unsupervised learning for data science[electronic resource] /edited by Michael W. Berry, Azlinah Mohamed, Bee Wah Yap. - Cham :Springer International Publishing :2020. - viii, 187 p. :ill., digital ;24 cm. - Unsupervised and semi-supervised learning,2522-848X. - Unsupervised and semi-supervised learning..

Chapter1: A Systematic Review on Supervised & Unsupervised Machine Learning Algorithms for Data Science -- Chapter2: Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints -- Chapter3: Distributed Single-Source Shortest Path Algorithms with Two Dimensional Graph Layout -- Chapter4: Using Non-Negative Tensor Decomposition for Unsupervised Textual Influence Modeling -- Chapter5: Survival Support Vector Machines: A Simulation Study and Its Health-related Application -- Chapter6: Semantic Unsupervised Learning for Word Sense Disambiguation -- Chapter7: Enhanced Tweet Hybrid Recommender System using Unsupervised Topic Modeling and Matrix Factorization based Neural Network -- Chapter8: New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering.

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: 9783030224752$q(electronic bk.)

Standard No.: 10.1007/978-3-030-22475-2doiSubjects--Topical Terms:

188639
Machine learning.

LC Class. No.: Q325.5 / .S86 2020

Dewey Class. No.: 006.31

Supervised and unsupervised learning for data science
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520 $a 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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