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Statistical learning and data sciences : third International Symposium, SLDS 2015, Egham, UK, April 20-23, 2015, Proceedings / Alexander Gammerman, Vladimir Vovk, Harris Papadopoulos (eds.).

By: Contributor(s): Material type: TextTextSeries: LNCS sublibrary. SL 7, Artificial intelligence. | Lecture notes in computer science. Lecture notes in artificial intelligence ; ; 9047.Publisher: Cham : Springer, 2015Description: 1 online resource (xiv, 444 pages) : illustrationsContent type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783319170916
  • 3319170910
Other title:
  • SLDS 2015
Subject(s): Genre/Form: Additional physical formats: Printed edition:: No title; Printed edition:: No titleDDC classification:
  • 006.3/1 23
LOC classification:
  • Q325.5 .S96 2015eb
Online resources:
Contents:
Intro; In memory of Alexey Chervonenkis; Preface; Organization; Contents; Invited Papers; Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer; 1 Introduction; 2 Learning with Intelligent Teacher: Privileged Information; 2.1 Classical Model of Learning; 2.2 LUPI Model of Learning; 3 Statistical Analysis of the Rate of Convergence; 3.1 Key Observation: SVM with Oracle Teacher; 3.2 From Ideal Oracle to Real Intelligent Teacher; 4 SVM+ for Similarity Control in LUPI Paradigm; 5 Three Examples of Similarity Control Using Privileged Information
5.1 Advanced Technical Model as Privileged Information5.2 Future Events as Privileged Information; 5.3 Holistic Description as Privileged Information; 6 Transfer of Knowledge Obtained in Privileged Information Space to Decision Space; 6.1 Knowledge Representation; 6.2 Scheme of Knowledge Transfer Between Spaces; Finding Fundamental Elements of Knowledge.; Fundamental Elements of Knowledge for Homogenous Quadratic Kernel.; Finding Images of Frames in Space X.; 6.3 Algorithms for Knowledge Transfer; 6.4 Kernels Involved in Intelligent Learning
6.5 Knowledge Transfer for Statistical Inference Problems6.6 General Remarks About Knowledge Transfer; What Knowledge Does Teacher Transfer?; What Are the Roots of Intelligence?; Holistic Description and Culture.; Quadratic Kernel.; Some Philosophical Interpretations.; 7 Conclusions; References; Statistical Inference Problems and Their Rigorous Solutions; 1 Basic Concepts of Classical Statistics; 1.1 Cumulative Distribution Function; 1.2 General Problems of Probability Theory and Statistics; 1.3 Empirical Cumulative Distribution Functions
1.4 The Glivenko-Cantelli Theorem and Kolmogorov Type Bounds1.5 Generalization to Multidimensional Case; 2 Main Problems of Statistical Inference; 2.1 Conditional Density, Conditional Probability, Regression, and Density Ratio Functions; 2.2 Direct Constructive Setting for Conditional Density Estimation; 2.3 Direct Constructive Setting for Conditional Probability Estimation; 2.4 Direct Constructive Setting for Regression Estimation; 2.5 Direct Constructive Setting of Density Ratio Estimation Problem; 3 Solution of Ill-Posed Operator Equations; 3.1 Fredholm Integral Equations of the First Kind
3.2 Methods of Solving Ill-Posed ProblemsInverse Operator Lemma.; Regularization Method.; 4 Stochastic Ill-Posed Problems; 4.1 Regularization of Stochastic Ill-Posed Problems; 4.2 Solution of Empirical Inference Problems; 5 Solving Statistical Inference Problems with V-matrix; 5.1 The V-matrix; Definition of Distance.; Definition of Distance for Conditional Probability Estimation Problem.; Distance for Regression Estimation Problem.; Distance for Density Ratio Estimation Problem.; 5.2 The Regularization Functionals in RKHS; Reproducing Kernel Hilbert Space.
In: Springer eBooksSummary: This book constitutes the refereed proceedings of the Third International Symposium on Statistical Learning and Data Sciences, SLDS 2015, held in Egham, Surrey, UK, April 2015. The 36 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on statistical learning and its applications, conformal prediction and its applications, new frontiers in data analysis for nuclear fusion, and geometric data analysis.
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International conference proceedings.

Includes author index.

Online resource; title from PDF title page (SpringerLink, viewed April 9, 2015).

This book constitutes the refereed proceedings of the Third International Symposium on Statistical Learning and Data Sciences, SLDS 2015, held in Egham, Surrey, UK, April 2015. The 36 revised full papers presented together with 2 invited papers were carefully reviewed and selected from 59 submissions. The papers are organized in topical sections on statistical learning and its applications, conformal prediction and its applications, new frontiers in data analysis for nuclear fusion, and geometric data analysis.

Intro; In memory of Alexey Chervonenkis; Preface; Organization; Contents; Invited Papers; Learning with Intelligent Teacher: Similarity Control and Knowledge Transfer; 1 Introduction; 2 Learning with Intelligent Teacher: Privileged Information; 2.1 Classical Model of Learning; 2.2 LUPI Model of Learning; 3 Statistical Analysis of the Rate of Convergence; 3.1 Key Observation: SVM with Oracle Teacher; 3.2 From Ideal Oracle to Real Intelligent Teacher; 4 SVM+ for Similarity Control in LUPI Paradigm; 5 Three Examples of Similarity Control Using Privileged Information

5.1 Advanced Technical Model as Privileged Information5.2 Future Events as Privileged Information; 5.3 Holistic Description as Privileged Information; 6 Transfer of Knowledge Obtained in Privileged Information Space to Decision Space; 6.1 Knowledge Representation; 6.2 Scheme of Knowledge Transfer Between Spaces; Finding Fundamental Elements of Knowledge.; Fundamental Elements of Knowledge for Homogenous Quadratic Kernel.; Finding Images of Frames in Space X.; 6.3 Algorithms for Knowledge Transfer; 6.4 Kernels Involved in Intelligent Learning

6.5 Knowledge Transfer for Statistical Inference Problems6.6 General Remarks About Knowledge Transfer; What Knowledge Does Teacher Transfer?; What Are the Roots of Intelligence?; Holistic Description and Culture.; Quadratic Kernel.; Some Philosophical Interpretations.; 7 Conclusions; References; Statistical Inference Problems and Their Rigorous Solutions; 1 Basic Concepts of Classical Statistics; 1.1 Cumulative Distribution Function; 1.2 General Problems of Probability Theory and Statistics; 1.3 Empirical Cumulative Distribution Functions

1.4 The Glivenko-Cantelli Theorem and Kolmogorov Type Bounds1.5 Generalization to Multidimensional Case; 2 Main Problems of Statistical Inference; 2.1 Conditional Density, Conditional Probability, Regression, and Density Ratio Functions; 2.2 Direct Constructive Setting for Conditional Density Estimation; 2.3 Direct Constructive Setting for Conditional Probability Estimation; 2.4 Direct Constructive Setting for Regression Estimation; 2.5 Direct Constructive Setting of Density Ratio Estimation Problem; 3 Solution of Ill-Posed Operator Equations; 3.1 Fredholm Integral Equations of the First Kind

3.2 Methods of Solving Ill-Posed ProblemsInverse Operator Lemma.; Regularization Method.; 4 Stochastic Ill-Posed Problems; 4.1 Regularization of Stochastic Ill-Posed Problems; 4.2 Solution of Empirical Inference Problems; 5 Solving Statistical Inference Problems with V-matrix; 5.1 The V-matrix; Definition of Distance.; Definition of Distance for Conditional Probability Estimation Problem.; Distance for Regression Estimation Problem.; Distance for Density Ratio Estimation Problem.; 5.2 The Regularization Functionals in RKHS; Reproducing Kernel Hilbert Space.

English.

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