Statistical Learning and Data Science

Statistical Learning and Data Science

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Features

    • Discusses a range of different aspects of statistical learning and data analysis
    • Covers unsupervised data analysis, supervised machine learning, and semi-supervised methods
    • Addresses data problems and knowledge extraction throughout the text
    • Points to new research directions and future developments

    Summary

    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.

    Table of Contents

    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

    Author Bio(s)

     
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