Statistical learning and data science
- Boca Raton : CRC Press, c2012.
- Physical description
- xv, 227 p. : col. ill. ; 27 cm.
- Series in computer science and data analysis.
Q325.5 .S73 2012
- Unknown Q325.5 .S73 2012
- Summa, Mireille Gettler.
- Includes bibliographical references (p. 205-223) and index.
- Statistical and Machine Learning Mining on Social Networks Benjamin Chapus, Francoise Fogelman Soulie, Erik Marcade, 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 Leon 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 Benzecri 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 Frederic 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 Severine 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, Frederic Ferraty, and Adela Martinez Calvo Introduction FDA Background FDA: a Useful Statistical Tool in Numerous Fields of Application Conclusions Methodological Richness of Functional Data Analysis Wenceslao Gonzalez Manteiga, and Philippe Vieu Introduction Spectral Analysis: Benchmark Methods in FDA Exploratory Methods in FDA Explanatory Methods in FDA Complementary Bibliography Conclusions Bibliography Index.
- (source: Nielsen Book Data)
- Publisher's 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.
(source: Nielsen Book Data)
- Publication date
- edited by Mireille Gettler Summa ... [et al.].
- Chapman & Hall/CRC computer science & data analysis
- 9781439867631 (hardback : alk. paper)
- 1439867631 (hardback : alk. paper)