Statistical Learning and Data Science

Herausgeber: Summa, Mireille Gettler; Goldfarb, Bernard; Bottou, Leon

• Broschiertes Buch

Produktbeschreibung

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.

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Produktdetails

• Verlag: Taylor & Francis Ltd (Sales)
• Seitenzahl: 243
• Erscheinungstermin: 23. September 2019
• Englisch
• Abmessung: 251mm x 175mm x 18mm
• Gewicht: 953g
• ISBN-13: 9780367381899
• ISBN-10: 0367381893
• Artikelnr.: 57816655

Autorenporträt

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

Inhaltsangabe

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.