000			06425cam a2200589Ii 4500
001			ocn930600937
003			OCoLC
005			20190328114813.0
006			m o d
007			cr cnu---unuuu
008			151130s2016 enk ob 001 0 eng d
040			_aN$T _beng _erda _epn _cN$T _dYDXCP _dN$T _dEBLCP _dCDX _dOPELS _dIDEBK _dTEFOD _dDEBSZ _dLOA _dNRC _dOCLCQ _dU3W _dMERUC _dD6H _dOCLCF _dCEF _dOCLCQ _dWYU _dTKN _dCNO
019			_a931590692 _a932332627
020			_a9780081006511 _q(electronic bk.)
020			_a0081006519 _q(electronic bk.)
020			_z9780128037324
035			_a(OCoLC)930600937 _z(OCoLC)931590692 _z(OCoLC)932332627
050		4	_aQA76.9.B45 _bL58 2016eb
072		7	_aMAT _x003000 _2bisacsh
072		7	_aMAT _x029000 _2bisacsh
082	0	4	_a519.5 _223
100	1		_aLiu, Shen, _eauthor.
245	1	0	_aComputational and statistical methods for analysing big data with applications / _h[electronic resource] _cShen Liu, James McGree, Zongyuan Ge, Yang Xie.
264		1	_aLondon : _bAcademic Press, _c2016.
264		4	_c�2016
300			_a1 online resource (viii, 194 pages) : _billustrations (some color)
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
588	0		_aOnline resource; title from PDF title page (EBSCO, viewed December 3, 2015).
500			_a"Academic Press is an imprint of Elsevier."
504			_aIncludes bibliographical references and index.
505	0		_aFront 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		_a2.1.3 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		_aR 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		_a4.4.2 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		_a5 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			_aDue 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	_aBig data.
650		0	_aQuantitative research.
650		0	_aQuantitative research _xStatistical methods.
650		0	_aData mining _xStatistical methods.
650		7	_aMATHEMATICS _xApplied. _2bisacsh
650		7	_aMATHEMATICS _xProbability & Statistics _xGeneral. _2bisacsh
650		7	_aBig data. _2fast _0(OCoLC)fst01892965
655		4	_aElectronic books.
700	1		_aMcGree, James, _eauthor.
700	1		_aGe, Zongyuan, _eauthor.
700	1		_aXie, Yang, _eauthor.
776	0	8	_iPrint version: _aLiu, Shen. _tComputational and Statistical Methods for Analysing Big Data with Applications. _d: Elsevier Science, �2015 _z9780128037324
856	4	0	_3ScienceDirect _uhttp://www.sciencedirect.com/science/book/9780128037324
999			_c247234 _d247234