Statistical Learning and Data Science

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.

Statistical and Machine LearningMining on Social NetworksBenjamin Chapus, Françoise Fogelman Soulié, Erik Marcadé, and Julien SauvageIntroductionWhat is a Social Network?KXEN’s Approach for Modeling Networked DataApplicationsConclusionLarge-Scale Machine Learning with Stochastic Gradient DescentLéon BottouIntroductionLearning with Gradient Descent Learning with Large Training Sets Efficient Learning ExperimentsFast Optimization Algorithms for Solving SVM+Dmitry Pechyony and Vladimir VapnikIntroductionSparse Line Search Algorithms Conjugate Sparse Line Search Proof of Convergence Properties of aSMO, caSMO ExperimentsConclusionsConformal Predictors in Semi-Supervised CaseDmitry Adamskiy, Ilia Nouretdinov and Alexander GammermanIntroductionBackground: Conformal Prediction for Supervised Learning Conformal Prediction for Semi-Supervised Learning ConclusionSome Properties of Infinite VC-Dimension SystemsAlexey ChervonenkisPreliminariesMain Assertion Additional Definitions The Restriction Process The ProofData Science, Foundations and ApplicationsChoriogenesisJean-Paul BenzécriIntroductionPreorderSpikePreorder and SpikeGeometry of the SpikeKatabasis: Spikes and FiltersProduct of Two or More SpikesCorrespondence Analysis: EpimixiaChoriogenesis, Coccoleiosis, CosmologyGDA in a Social Science Research Program: The Case of Bourdieu’s SociologyFrédéric LebaronIntroductionBourdieu and Statistics From Multidimensionality to GeometryInvestigating Fields A Sociological Research Program ConclusionSemantics from Narrative: State of the Art and Future ProspectsFionn Murtagh, Adam Ganz, and Joe ReddingtonIntroduction: Analysis of Narrative Deeper Look at Semantics in Casablanca ScriptFrom Filmscripts to Scholarly Research ArticlesConclusionsMeasuring Classifier PerformanceDavid J. HandIntroductionBackgroundThe Area under the CurveIncoherence of the Area under the CurveWhat to Do about ItDiscussionA Clustering Approach to Monitor System WorkingAlzennyr Da Silva, Yves Lechevallier, and Redouane SeraouiIntroductionRelated WorkClustering Approach for Monitoring System WorkingExperimentsConclusionIntroduction to Molecular PhylogenyMahendra Mariadassou and Avner Bar-HenThe Context Of Molecular PhylogenyMethods For Reconstructing Phylogenetic TreesValidation of Phylogenetic TreesBayesian analysis of Structural Equation Models using Parameter ExpansionSéverine Demeyer, Jean-Louis Foulley, Nicolas Fischer, and Gilbert SaportaIntroductionSpecification of SEM for Mixed Observed VariablesBayesian Estimation of SEMs with Mixed Observed VariablesApplication: Modeling Expert Knowledge in Uncertainty AnalysisConclusion and PerspectivesComplex DataClustering Trajectories of a Three-Way Longitudinal Data SetMireille Gettler Summa, Bernard Goldfarb, and Maurizio VichiIntroductionNotationTrajectoriesDissimilarities between TrajectoriesThe Clustering ProblemApplicationConclusionsTrees with Soft NodesAntonio CiampiIntroductionTrees for Symbolic DataSoft NodesTrees with Soft NodesExamplesEvaluationDiscussionSynthesis of ObjectsMyriam Touati, Mohamed Djedour, and Edwin DidayIntroductionSome Symbolic Object DefinitionsGeneralizationBackground KnowledgeThe ProblemDynamic Clustering Algorithm on Symbolic Objects: SYNTHOAlgorithm of Generalization: GENOSApplication: Advising the University of Algiers StudentsConclusionFunctional Data Analysis: An Interdisciplinary Statistical TopicLaurent Delsol, Frédéric Ferraty, and Adela Martínez CalvoIntroductionFDA BackgroundFDA: a Useful Statistical Tool in Numerous Fields of ApplicationConclusionsMethodological Richness of Functional Data AnalysisWenceslao Gonzàlez Manteiga, and Philippe VieuIntroductionSpectral Analysis: Benchmark Methods in FDAExploratory Methods in FDAExplanatory Methods in FDAComplementary BibliographyConclusionsBibliographyIndex