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Computational and statistical methods for analysing big data with applications /
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...
| Clasificación: | Libro Electrónico |
|---|---|
| Autores principales: | , , , |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
London :
Academic Press,
[2016]
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| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- 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.
- 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.
- 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.
- 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.
- 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.