Computational and statistical methods for analysing big data with applications / (Record no. 247234)
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| control field | ocn930600937 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | OCoLC |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20190328114813.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu---unuuu |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 151130s2016 enk ob 001 0 eng d |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | N$T |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | N$T |
| Modifying agency | YDXCP |
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| -- | CDX |
| -- | OPELS |
| -- | IDEBK |
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| -- | DEBSZ |
| -- | LOA |
| -- | NRC |
| -- | OCLCQ |
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| 019 ## - | |
| -- | 931590692 |
| -- | 932332627 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9780081006511 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 0081006519 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9780128037324 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)930600937 |
| Canceled/invalid control number | (OCoLC)931590692 |
| -- | (OCoLC)932332627 |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | QA76.9.B45 |
| Item number | L58 2016eb |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | MAT |
| Subject category code subdivision | 003000 |
| Source | bisacsh |
| 072 #7 - SUBJECT CATEGORY CODE | |
| Subject category code | MAT |
| Subject category code subdivision | 029000 |
| Source | bisacsh |
| 082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 519.5 |
| Edition number | 23 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Liu, Shen, |
| Relator term | author. |
| 245 10 - TITLE STATEMENT | |
| Title | Computational and statistical methods for analysing big data with applications / |
| Medium | [electronic resource] |
| Statement of responsibility, etc. | Shen Liu, James McGree, Zongyuan Ge, Yang Xie. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | London : |
| Name of producer, publisher, distributor, manufacturer | Academic Press, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2016. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | �2016 |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (viii, 194 pages) : |
| Other physical details | illustrations (some color) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 588 0# - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Online resource; title from PDF title page (EBSCO, viewed December 3, 2015). |
| 500 ## - GENERAL NOTE | |
| General note | "Academic Press is an imprint of Elsevier." |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE | |
| Bibliography, etc | Includes bibliographical references and index. |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Front Cover; Computational and Statistical Methods for Analysing Big Data with Applications; Copyright Page; Contents; List of Figures; List of Tables; Acknowledgment; 1 Introduction; 1.1 What is big data?; 1.1.1 Volume; 1.1.2 Velocity; 1.1.3 Variety; 1.1.4 Another two V's; 1.2 What is this book about?; 1.3 Who is the intended readership?; References; 2 Classification methods; 2.1 Fundamentals of classification; 2.1.1 Features and training samples; Example: Discriminating owners from non-owners of riding mowers; 2.1.2 Probabilities of misclassification and the associated costs. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 2.1.3 Classification by minimizing the ECMExample: Medical diagnosis; 2.1.4 More than two classes; 2.2 Popular classifiers for analysing big data; 2.2.1 k-Nearest neighbour algorithm; 2.2.2 Regression models; 2.2.3 Bayesian networks; 2.2.4 Artificial neural networks; 2.2.5 Decision trees; 2.3 Summary; References; 3 Finding groups in data; 3.1 Principal component analysis; 3.2 Factor analysis; 3.3 Cluster analysis; 3.3.1 Hierarchical clustering procedures; 3.3.2 Nonhierarchical clustering procedures; 3.3.3 Deciding on the number of clusters; 3.4 Fuzzy clustering; Appendix. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | R code for principal component analysis and factor analysisMATLAB code for cluster analysis; References; 4 Computer vision in big data applications; 4.1 Big datasets for computer vision; 4.2 Machine learning in computer vision; 4.2.1 Feature engineering; 4.2.2 Classifiers; Regression; Support vector machine; Gaussian mixture models; 4.3 State-of-the-art methodology: deep learning; 4.3.1 A single-neuron model; 4.3.2 A multilayer neural network; 4.3.3 Training process of multilayer neural networks; Feed-forward pass; Back-propagation pass; 4.4 Convolutional neural networks; 4.4.1 Pooling. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 4.4.2 Training a CNN4.4.3 An example of CNN in image recognition; Overall structure of the network; Data preprocessing; Prevention of overfitting; 4.5 A tutorial: training a CNN by ImageNet; 4.5.1 Caffe; 4.5.2 Architecture of the network; Input layer; Convolutional layer; Pooling layer; LRN layer; Fully-connected layers; Dropout layers; Softmax layer; 4.5.3 Training; 4.6 Big data challenge: ILSVRC; 4.6.1 Performance evaluation; 4.6.2 Winners in the history of ILSVRC; 4.7 Concluding remarks: a comparison between human brains and computers; Acknowledgements; References. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 5 A computational method for analysing large spatial datasets5.1 Introduction to spatial statistics; 5.1.1 Spatial dependence; 5.1.2 Cross-variable dependence; 5.1.3 Limitations of conventional approaches to spatial analysis; 5.2 The HOS method; 5.2.1 Cross-variable high-order statistics; 5.2.2 Searching process; 5.2.3 Local CPDF approximation; 5.3 MATLAB functions for the implementation of the HOS method; 5.3.1 Spatial template and searching process; 5.3.2 Higher-order statistics; 5.3.3 Coefficients of Legendre polynomials; 5.3.4 CPDF approximation; 5.4 A case study; References. |
| 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. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Quantitative research. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Quantitative research |
| General subdivision | Statistical methods. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Data mining |
| General subdivision | Statistical methods. |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | MATHEMATICS |
| General subdivision | Applied. |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | MATHEMATICS |
| General subdivision | Probability & Statistics |
| -- | General. |
| Source of heading or term | bisacsh |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name as entry element | Big data. |
| Source of heading or term | fast |
| Authority record control number | (OCoLC)fst01892965 |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | McGree, James, |
| Relator term | author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Ge, Zongyuan, |
| Relator term | author. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Xie, Yang, |
| Relator term | author. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | Liu, Shen. |
| Title | Computational and Statistical Methods for Analysing Big Data with Applications. |
| Place, publisher, and date of publication | : Elsevier Science, �2015 |
| International Standard Book Number | 9780128037324 |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Materials specified | ScienceDirect |
| Uniform Resource Identifier | http://www.sciencedirect.com/science/book/9780128037324 |
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