Measures of Complexity Festschrift for Alexey Chervonenkis /

This book brings together historical notes, reviews of research developments, fresh ideas on how to make VC (Vapnik-Chervonenkis) guarantees tighter, and new technical contributions in the areas of machine learning, statistical inference, classification, algorithmic statistics, and pattern recogniti...

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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor Corporativo: SpringerLink (Online service)
Otros Autores: Vovk, Vladimir (Editor ), Papadopoulos, Harris (Editor ), Gammerman, Alexander (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Cham : Springer International Publishing : Imprint: Springer, 2015.
Edición:1st ed. 2015.
Temas:
Artificial intelligence.
Statistics .
Computer science-Mathematics.
Mathematical statistics.
Mathematical optimization.
Artificial Intelligence.
Statistical Theory and Methods.
Probability and Statistics in Computer Science.
Optimization.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • Chervonenkis's Recollections
  • A Paper That Created Three New Fields
  • On the Uniform Convergence of Relative Frequencies of Events to Their Probabilities
  • Sketched History: VC Combinatorics, 1826 up to 1975
  • Institute of Control Sciences through the Lens of VC Dimension
  • VC Dimension, Fat-Shattering Dimension, Rademacher Averages, and Their Applications
  • Around Kolmogorov Complexity: Basic Notions and Results
  • Predictive Complexity for Games with Finite Outcome Spaces
  • Making Vapnik-Chervonenkis Bounds Accurate
  • Comment: Transductive PAC-Bayes Bounds Seen as a Generalization of Vapnik-Chervonenkis Bounds
  • Comment: The Two Styles of VC Bounds
  • Rejoinder: Making VC Bounds Accurate
  • Measures of Complexity in the Theory of Machine Learning
  • Classes of Functions Related to VC Properties
  • On Martingale Extensions of Vapnik-Chervonenkis
  • Theory with Applications to Online Learning
  • Measuring the Capacity of Sets of Functions in the Analysis of ERM
  • Algorithmic Statistics Revisited
  • Justifying Information-Geometric Causal Inference
  • Interpretation of Black-Box Predictive Models
  • PAC-Bayes Bounds for Supervised Classification
  • Bounding Embeddings of VC Classes into Maximum Classes
  • Algorithmic Statistics Revisited
  • Justifying Information-Geometric Causal Inference
  • Interpretation of Black-Box Predictive Models
  • PAC-Bayes Bounds for Supervised Classification
  • Bounding Embeddings of VC Classes into Maximum Classes
  • Strongly Consistent Detection for Nonparametric Hypotheses
  • On the Version Space Compression Set Size and Its Applications
  • Lower Bounds for Sparse Coding
  • Robust Algorithms via PAC-Bayes and Laplace Distributions
  • Postscript: Tragic Death of Alexey Chervonenkis
  • Credits
  • Index.

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