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.
Statistical and Machine Learning: Mining on Social Networks. Large-Scale Machine Learning with Stochastic Gradient Descent. Fast Optimization Algorithms for Solving SVM+. Conformal Predictors in Semi-Supervised Case. Some Properties of Infinite VC-Dimension Systems.
Data Science, Foundations and Applications: Choriogenesis. GDA in a Social Science Research Program: The Case of Bourdieu’s Sociology. Semantics from Narrative: State of the Art and Future Prospects. Measuring Classifier Performance. A Clustering Approach to Monitor System Working. Introduction to Molecular Phylogeny. Bayesian analysis of Structural Equation Models using Parameter Expansion.
Complex Data: Clustering Trajectories of a Three-Way Longitudinal Data Set. Trees with Soft Nodes. Synthesis of Objects. Functional Data Analysis: An Interdisciplinary Statistical Topic. Methodological Richness of Functional Data Analysis.