Computational and statistical methods for analysing big data with applications (Record no. 201882)

000 -LEADER
fixed length control field 03834 a2200229 4500
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION
fixed length control field 160114b2016 xxu||||| |||| 00| 0 eng d
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number 9780128037324
082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER
Item number L4C6
Classification number 005.74015
100 ## - MAIN ENTRY--PERSONAL NAME
Personal name Liu, Shen
9 (RLIN) 326909
245 ## - TITLE STATEMENT
Title Computational and statistical methods for analysing big data with applications
260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT)
Place of publication, distribution, etc Amsterdam
Name of publisher, distributor, etc Academic Press
Date of publication, distribution, etc 2016
300 ## - PHYSICAL DESCRIPTION
Extent viii, 194 p.
504 ## - BIBLIOGRAPHY, ETC. NOTE
Bibliography, etc Table of Contents:

1. Introduction

Abstract
1.1 What is big data?
1.2 What is this book about?
1.3 Who is the intended readership?
References

2. Classification methods

Abstract
2.1 Fundamentals of classification
2.2 Popular classifiers for analysing big data
2.3 Summary
References

3. Finding groups in data

Abstract
3.1 Principal component analysis
3.2 Factor analysis
3.3 Cluster analysis
3.4 Fuzzy clustering
Appendix
References

4. Computer vision in big data applications

Abstract
4.1 Big datasets for computer vision
4.2 Machine learning in computer vision
4.3 State-of-the-art methodology: deep learning
4.4 Convolutional neural networks
4.5 A tutorial: training a CNN by ImageNet
4.6 Big data challenge: ILSVRC
4.7 Concluding remarks: a comparison between human brains and computers
Acknowledgements
References

5. A computational method for analysing large spatial datasets

Abstract
5.1 Introduction to spatial statistics
5.2 The HOS method
5.3 MATLAB functions for the implementation of the HOS method
5.4 A case study
References

6. Big data and design of experiments

Abstract
6.1 Introduction
6.2 Overview of experimental design
6.3 Mortgage Default Example
6.4 U.S.A domestic Flight Performance – Airline Example
6.5 Conclusion
References

7. Big data in healthcare applications

Abstract
7.1 Big data in healthcare-related fields
7.2 Predicting days in hospital (DIH) using health insurance claims: a case study
Acknowledgement
References

8. Big data from mobile devices

Abstract
8.1 Data from wearable devices for health monitoring
8.2 Mobile devices in transportation

520 ## - SUMMARY, ETC.
Summary, etc Due to the scale and complexity of data sets currently being collected in areas such as health, transportation, environmental science, engineering, information technology, business and finance, modern quantitative analysts are seeking improved and appropriate computational and statistical methods to explore, model and draw inferences from big data. This book aims to introduce suitable approaches for such endeavours, providing applications and case studies for the purpose of demonstration.

Computational and Statistical Methods for Analysing Big Data with Applications starts with an overview of the era of big data. It then goes onto explain the computational and statistical methods which have been commonly applied in the big data revolution. For each of these methods, an example is provided as a guide to its application. Five case studies are presented next, focusing on computer vision with massive training data, spatial data analysis, advanced experimental design methods for big data, big data in clinical medicine, and analysing data collected from mobile devices, respectively. The book concludes with some final thoughts and suggested areas for future research in big data.


(http://store.elsevier.com/product.jsp?isbn=9780128037324&_requestid=263240)
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Big data
9 (RLIN) 326910
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Data mining - Statistical methods
9 (RLIN) 326911
650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name as entry element Mathematics - Applied
9 (RLIN) 326912
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name McGree, James
9 (RLIN) 326913
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Ge, Zongyuan
9 (RLIN) 326914
700 ## - ADDED ENTRY--PERSONAL NAME
Personal name Xie, Yang
9 (RLIN) 326915
942 ## - ADDED ENTRY ELEMENTS (KOHA)
Source of classification or shelving scheme
Item type Books
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Collection code Permanent location Current location Shelving location Date acquired Source of acquisition Cost, normal purchase price Item location Total Checkouts Total Renewals Full call number Barcode Date last seen Date last borrowed Cost, replacement price Koha item type
          Non-fiction Vikram Sarabhai Library Vikram Sarabhai Library   2016-01-14 Kushal Books 5613.19 Slot 87 (0 Floor, West Wing) 1 2 005.74015 L4C6 190856 2016-12-07 2016-01-15 7016.49 Books

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