Statistical Learning and Data Science

ISBN-13: 9780367381899 / Angielski / Miękka / 2019 / 243 str.

Mireille Gettler Summa; Leon Bottou; Bernard Goldfarb

Opis

Driven by a vast range of applications, data analysis and learning from data are vibrant areas of research. Various methodologies, including unsupervised data analysis, supervised machine learning, and semi-supervised techniques, have continued to develop to cope with the increasing amount of data collected through modern technology. With a focus on applications, this volume presents contributions from some of the leading researchers in the different fields of data analysis. Synthesizing the methodologies into a coherent framework, the book covers a range of topics, from large-scale machine learning to synthesis objects analysis.

Spis treści

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.

O autorze

Mireille Gettler Summa, Léon Bottou, Bernard Goldfarb, Fionn Murtagh, Catherine Pardoux, Myriam Touati