Statistical Learning and Data Science (eBook, ePUB)

Redaktion: Summa, Mireille Gettler; Touati, Myriam; Pardoux, Catherine; Murtagh, Fionn; Goldfarb, Bernard; Bottou, Leon

• Format: ePub

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 eBooks
• Erscheinungstermin: 19. Dezember 2011
• Englisch
• ISBN-13: 9781040059692
• Artikelnr.: 72274037

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.