E-Book, Englisch, 243 Seiten
Summa / Bottou / Goldfarb Statistical Learning and Data Science
1. Auflage 2012
ISBN: 978-1-4398-6764-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
E-Book, Englisch, 243 Seiten
Reihe: Chapman & Hall/CRC Computer Science & Data Analysis
ISBN: 978-1-4398-6764-8
Verlag: Taylor & Francis
Format: PDF
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)
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.
Zielgruppe
Researchers and graduate students in statistics, computer science, electrical engineering, management science, social science, and bioscience.
Autoren/Hrsg.
Fachgebiete
Weitere Infos & Material
Statistical and Machine Learning
Mining on Social Networks
Benjamin Chapus, Françoise Fogelman Soulié, Erik Marcadé, and Julien Sauvage
Introduction
What is a Social Network?
KXEN’s Approach for Modeling Networked Data
Applications
Conclusion
Large-Scale Machine Learning with Stochastic Gradient Descent
Léon Bottou
Introduction
Learning with Gradient Descent
Learning with Large Training Sets
Efficient Learning
Experiments
Fast Optimization Algorithms for Solving SVM+
Dmitry Pechyony and Vladimir Vapnik
Introduction
Sparse Line Search Algorithms
Conjugate Sparse Line Search
Proof of Convergence Properties of aSMO, caSMO
Experiments
Conclusions
Conformal Predictors in Semi-Supervised Case
Dmitry Adamskiy, Ilia Nouretdinov and Alexander Gammerman
Introduction
Background: Conformal Prediction for Supervised Learning
Conformal Prediction for Semi-Supervised Learning
Conclusion
Some Properties of Infinite VC-Dimension Systems
Alexey Chervonenkis
Preliminaries
Main Assertion
Additional Definitions
The Restriction Process
The Proof
Data Science, Foundations and Applications
Choriogenesis
Jean-Paul Benzécri
Introduction
Preorder
Spike
Preorder and Spike
Geometry of the Spike
Katabasis: Spikes and Filters
Product of Two or More Spikes
Correspondence Analysis: Epimixia
Choriogenesis, Coccoleiosis, Cosmology
GDA in a Social Science Research Program: The Case of Bourdieu’s Sociology
Frédéric Lebaron
Introduction
Bourdieu and Statistics
From Multidimensionality to Geometry
Investigating Fields
A Sociological Research Program
Conclusion
Semantics from Narrative: State of the Art and Future Prospects
Fionn Murtagh, Adam Ganz, and Joe Reddington
Introduction: Analysis of Narrative
Deeper Look at Semantics in Casablanca Script
From Filmscripts to Scholarly Research Articles
Conclusions
Measuring Classifier Performance
David J. Hand
Introduction
Background
The Area under the Curve
Incoherence of the Area under the Curve
What to Do about It
Discussion
A Clustering Approach to Monitor System Working
Alzennyr Da Silva, Yves Lechevallier, and Redouane Seraoui
Introduction
Related Work
Clustering Approach for Monitoring System Working
Experiments
Conclusion
Introduction to Molecular Phylogeny
Mahendra Mariadassou and Avner Bar-Hen
The Context Of Molecular Phylogeny
Methods For Reconstructing Phylogenetic Trees
Validation of Phylogenetic Trees
Bayesian analysis of Structural Equation Models using Parameter Expansion
Séverine Demeyer, Jean-Louis Foulley, Nicolas Fischer, and Gilbert Saporta
Introduction
Specification of SEM for Mixed Observed Variables
Bayesian Estimation of SEMs with Mixed Observed Variables
Application: Modeling Expert Knowledge in Uncertainty Analysis
Conclusion and Perspectives
Complex Data
Clustering Trajectories of a Three-Way Longitudinal Data Set
Mireille Gettler Summa, Bernard Goldfarb, and Maurizio Vichi
Introduction
Notation
Trajectories
Dissimilarities between Trajectories
The Clustering Problem
Application
Conclusions
Trees with Soft Nodes
Antonio Ciampi
Introduction
Trees for Symbolic Data
Soft Nodes
Trees with Soft Nodes
Examples
Evaluation
Discussion
Synthesis of Objects
Myriam Touati, Mohamed Djedour, and Edwin Diday
Introduction
Some Symbolic Object Definitions
Generalization
Background Knowledge
The Problem
Dynamic Clustering Algorithm on Symbolic Objects: SYNTHO
Algorithm of Generalization: GENOS
Application: Advising the University of Algiers Students
Conclusion
Functional Data Analysis: An Interdisciplinary Statistical Topic
Laurent Delsol, Frédéric Ferraty, and Adela Martínez Calvo
Introduction
FDA Background
FDA: a Useful Statistical Tool in Numerous Fields of Application
Conclusions
Methodological Richness of Functional Data Analysis
Wenceslao Gonzàlez Manteiga, and Philippe Vieu
Introduction
Spectral Analysis: Benchmark Methods in FDA
Exploratory Methods in FDA
Explanatory Methods in FDA
Complementary Bibliography
Conclusions
Bibliography
Index
