Concurrent users
Unlimited
Authorized users
Authorized users
Document Delivery Supplied
Can lend chapters, not whole ebooks
Title
Advances in machine learning for big data analysis / Satchidananda Dehuri, Yen-Wei Chen, editors.
ISBN
9789811689307 (electronic bk.)
981168930X (electronic bk.)
9811689296
9789811689291
Imprint
Singapore : Springer, 2022.
Language
English
Description
1 online resource (254 pages)
Other Standard Identifiers
10.1007/978-981-16-8930-7 doi
Call Number
Q325.5
Dewey Decimal Classification
006.3/1
Summary
This book focuses on research aspects of ensemble approaches of machine learning techniques that can be applied to address the big data problems. In this book, various advancements of machine learning algorithms to extract data-driven decisions from big data in diverse domains such as the banking sector, healthcare, social media, and video surveillance are presented in several chapters. Each of them has separate functionalities, which can be leveraged to solve a specific set of big data applications. This book is a potential resource for various advances in the field of machine learning and data science to solve big data problems with many objectives. It has been observed from the literature that several works have been focused on the advancement of machine learning in various fields like biomedical, stock prediction, sentiment analysis, etc. However, limited discussions have been carried out on application of advanced machine learning techniques in solving big data problems.
Bibliography, etc. Note
Includes bibliographical references.
Access Note
Access limited to authorized users.
Source of Description
Online resource; title from PDF title page (SpringerLink, viewed March 10, 2022).
Series
Intelligent systems reference library ; v. 218.
Deep Learning for Supervised Learning
Deep Learning for Unsupervised Learning
Support Vector Machine for Regression
Support Vector Machine for Classification
Decision Tree for Regression
Higher Order Neural Networks
Competitive Learning
Semi-supervised Learning
Multi-objective Optimization Techniques
Techniques for Feature Selection/Extraction
Techniques for Task Relevant Big Data Analysis
Techniques for Post Processing Task in Big Data Analysis
Customer Relationship Management.
