Statistical Learning and Data Science

by Mireille Gettler Summa (Editor), Leon Bottou (Editor), Bernard Goldfarb (Editor), Fionn Murtagh (Editor), Catherine Pardoux (Editor)

Overview

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 wor

Product Details

ISBN-13:	9781040059692
Publisher:	CRC Press
Publication date:	12/19/2011
Sold by:	Barnes & Noble
Format:	eBook
Pages:	243
File size:	2 MB

About the Author

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

Table of Contents

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.