Measures of Complexity

Festschrift for Alexey Chervonenkis

Inbunden, Engelska, 2015

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Beskrivning

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

Produktinformation

Utgivningsdatum:2015-09-14
Mått:155 x 235 x 29 mm
Vikt:811 g
Format:Inbunden
Språk:Engelska
Antal sidor:399
Upplaga:15001
Förlag:Springer International Publishing AG
ISBN:9783319218519

Utforska kategorier

Optimering inom Naturvetenskap och teknik
Systemvetenskap och AI inom Data och IT
Matematisk statistik inom Naturvetenskap och teknik

Innehållsförteckning

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.