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Measures of Complexity: Festschrift for Alexey Chervonenkis 1st ed. 2015 [Kõva köide]

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  • Formaat: Hardback, 399 pages, kõrgus x laius: 235x155 mm, kaal: 7686 g, 30 Illustrations, color; 17 Illustrations, black and white; XXXI, 399 p. 47 illus., 30 illus. in color., 1 Hardback
  • Ilmumisaeg: 14-Sep-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319218514
  • ISBN-13: 9783319218519
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Measures of Complexity: Festschrift for Alexey Chervonenkis 1st ed. 2015Suurem pilt
  • Formaat: Hardback, 399 pages, kõrgus x laius: 235x155 mm, kaal: 7686 g, 30 Illustrations, color; 17 Illustrations, black and white; XXXI, 399 p. 47 illus., 30 illus. in color., 1 Hardback
  • Ilmumisaeg: 14-Sep-2015
  • Kirjastus: Springer International Publishing AG
  • ISBN-10: 3319218514
  • ISBN-13: 9783319218519
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783319218519.html
Märksõnad:

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, optimization, computational complexity, communication complexity, inference, classification, algorithmic statistics, and pattern recognition.

The contributions are leading scientists in domains such as statistics, theoretical computer science, and mathematics, and the book will be of interest to researchers and graduate students in these domains.

Chervonenkiss 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
VapnikChervonenkis Bounds Accurate.- Comment: Transductive PAC-Bayes Bounds
Seen as a Generalization of VapnikChervonenkis 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 VapnikChervonenkis.- 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.