Statistical Learning And Data Science (chapman & Hall/crc Computer Science & Data Analysis)

"Data analysis is changing fast. Driven by a vast range of application domains and affordable tools, machine learning has become mainstream. Unsupervised data analysis, including cluster analysis, factor analysis, and low dimensionality mapping methods continually being updated, have reached new heights of achievement in the incredibly rich data world that we inhabit. Statistical Learning and Data Science is a work of reference in the rapidly evolving context of converging methodologies. It gathers contributions from some of the foundational thinkers in the different fields of data analysis to the major theoretical results in the domain. On the methodological front, the volume includes conformal prediction and frameworks for assessing confidence in outputs, together with attendant risk. It illustrates a wide range of applications, including semantics, credit risk, energy production, genomics, and ecology. The book also addresses issues of origin and evolutions in the unsupervised data analysis arena, and presents some approaches for time series, symbolic data, and functional data. Over the history of multidimensional data analysis, more and more complex data have become available for processing. Supervised machine learning, semi-supervised analysis approaches, and unsupervised data analysis, provide great capability for addressing the digital data deluge. Exploring the foundations and recent breakthroughs in the field, Statistical Learning and Data Science demonstrates how data analysis can improve personal and collective health and the well-being of our social, business, and physical environments."-- Provided by publisher Title Page ......Page 4 Contents......Page 6 Preface......Page 8 Contributors......Page 14 I. Statistical and Machine Learning......Page 18 1. Mining on Social Networks......Page 20 2. Large-Scale Machine Learning with Stochastic Gradient Descent......Page 34 3. Fast Optimization Algorithms for Solving SVM+......Page 44 4. Conformal Predictors in Semisupervised Case......Page 60 5. Some Properties of Infinite VC-Dimension Systems......Page 70 II. Data Science, Foundations, and Applications......Page 78 6. Choriogenesis: the Dynamical Genesis of Space and Its Dimensions, Controlled by Correspondence Analysis......Page 80 7. Geometric Data Analysis in a Social Science Research Program: The Case of Bourdieu's Sociology......Page 94 8. Semantics from Narrative: State of the Art and Future Prospects......Page 108 9. Measuring Classifier Performance: On the Incoherence of the Area under the ROC Curve and What to Do about It......Page 120 10. A Clustering Approach to Monitor System Working: An Application to Electric Power Production......Page 130 11. Introduction to Molecular Phylogeny......Page 142 12. Bayesian Analysis of Structural Equation Models Using Parameter Expansion......Page 152 III. Complex Data......Page 164 13. Clustering Trajectories of a Three-Way Longitudinal Dataset......Page 166 14. Trees with Soft Nodes: A New Approach to the Construction of Prediction Trees from Data......Page 176 15. Synthesis of Objects......Page 188 16. Functional Data Analysis: An Interdisciplinary Statistical Topic......Page 206 17. Methodological Richness of Functional Data Analysis......Page 214 Bibliography......Page 222