Statistical Learning and Data Science

Statistical Learning and Data Science

by Mireille Gettler Summa, Leon Bottou, Bernard Goldfarb, Fionn Murtagh, Catherine Pardoux, Myriam Touati
Released December 2011
Publisher(s): Chapman and Hall/CRC
ISBN: 9781439867648

Read it now on the O’Reilly learning platform with a 10-day free trial.

O’Reilly members get unlimited access to books, live events, courses curated by job role, and more from O’Reilly and nearly 200 top publishers.

Start your free trial

Book description

Driven by a vast range of applications, data analysis and learning from data are vibrant areas of research. Various methodologies, including unsupervised data analysis, supervised machine learning, and semi-supervised techniques, have continued to develop to cope with the increasing amount of data collected through modern technology. With a focus on applications, this volume presents contributions from some of the leading researchers in the different fields of data analysis. Synthesizing the methodologies into a coherent framework, the book covers a range of topics, from large-scale machine learning to synthesis objects analysis.

Table of contents

  1. Front Cover
  2. Contents
  3. Preface (1/2)
  4. Preface (2/2)
  5. Contributors
  6. I. Statistical and Machine Learning
    1. 1. Mining on Social Networks (1/3)
    2. 1. Mining on Social Networks (2/3)
    3. 1. Mining on Social Networks (3/3)
    4. 2. Large-Scale Machine Learning with Stochastic Gradient Descent (1/2)
    5. 2. Large-Scale Machine Learning with Stochastic Gradient Descent (2/2)
    6. 3. Fast Optimization Algorithms for Solving SVM+ (1/4)
    7. 3. Fast Optimization Algorithms for Solving SVM+ (2/4)
    8. 3. Fast Optimization Algorithms for Solving SVM+ (3/4)
    9. 3. Fast Optimization Algorithms for Solving SVM+ (4/4)
    10. 4. Conformal Predictors in Semisupervised Case (1/2)
    11. 4. Conformal Predictors in Semisupervised Case (2/2)
    12. 5. Some Properties of Infinite VC-Dimension Systems (1/2)
    13. 5. Some Properties of Infinite VC-Dimension Systems (2/2)
  7. II. Data Science, Foundations, and Applications
    1. 6. Choriogenesis: the Dynamical Genesis of Space and Its Dimensions, Controlled by Correspondence Analysis (1/3)
    2. 6. Choriogenesis: the Dynamical Genesis of Space and Its Dimensions, Controlled by Correspondence Analysis (2/3)
    3. 6. Choriogenesis: the Dynamical Genesis of Space and Its Dimensions, Controlled by Correspondence Analysis (3/3)
    4. 7. Geometric Data Analysis in a Social Science Research Program: The Case of Bourdieu's Sociology (1/3)
    5. 7. Geometric Data Analysis in a Social Science Research Program: The Case of Bourdieu's Sociology (2/3)
    6. 7. Geometric Data Analysis in a Social Science Research Program: The Case of Bourdieu's Sociology (3/3)
    7. 8. Semantics from Narrative: State of the Art and Future Prospects (1/3)
    8. 8. Semantics from Narrative: State of the Art and Future Prospects (2/3)
    9. 8. Semantics from Narrative: State of the Art and Future Prospects (3/3)
    10. 9. Measuring Classifier Performance: On the Incoherence of the Area under the ROC Curve and What to Do about It (1/2)
    11. 9. Measuring Classifier Performance: On the Incoherence of the Area under the ROC Curve and What to Do about It (2/2)
    12. 10. A Clustering Approach to Monitor System Working: An Application to Electric Power Production (1/3)
    13. 10. A Clustering Approach to Monitor System Working: An Application to Electric Power Production (2/3)
    14. 10. A Clustering Approach to Monitor System Working: An Application to Electric Power Production (3/3)
    15. 11. Introduction to Molecular Phylogeny (1/2)
    16. 11. Introduction to Molecular Phylogeny (2/2)
    17. 12. Bayesian Analysis of Structural Equation Models Using Parameter Expansion (1/3)
    18. 12. Bayesian Analysis of Structural Equation Models Using Parameter Expansion (2/3)
    19. 12. Bayesian Analysis of Structural Equation Models Using Parameter Expansion (3/3)
  8. III. Complex Data
    1. 13. Clustering Trajectories of a Three-Way Longitudinal Dataset (1/2)
    2. 13. Clustering Trajectories of a Three-Way Longitudinal Dataset (2/2)
    3. 14. Trees with Soft Nodes: A New Approach to the Construction of Prediction Trees from Data (1/3)
    4. 14. Trees with Soft Nodes: A New Approach to the Construction of Prediction Trees from Data (2/3)
    5. 14. Trees with Soft Nodes: A New Approach to the Construction of Prediction Trees from Data (3/3)
    6. 15. Synthesis of Objects (1/4)
    7. 15. Synthesis of Objects (2/4)
    8. 15. Synthesis of Objects (3/4)
    9. 15. Synthesis of Objects (4/4)
    10. 16. Functional Data Analysis: An Interdisciplinary Statistical Topic (1/2)
    11. 16. Functional Data Analysis: An Interdisciplinary Statistical Topic (2/2)
    12. 17. Methodological Richness of Functional Data Analysis (1/2)
    13. 17. Methodological Richness of Functional Data Analysis (2/2)
  9. Bibliography (1/5)
  10. Bibliography (2/5)
  11. Bibliography (3/5)
  12. Bibliography (4/5)
  13. Bibliography (5/5)

Product information

  • Title: Statistical Learning and Data Science
  • Author(s): Mireille Gettler Summa, Leon Bottou, Bernard Goldfarb, Fionn Murtagh, Catherine Pardoux, Myriam Touati
  • Release date: December 2011
  • Publisher(s): Chapman and Hall/CRC
  • ISBN: 9781439867648