04909nam 22006735 450 991029920940332120200630042508.03-319-21852-210.1007/978-3-319-21852-6(CKB)3710000000471393(EBL)4178438(SSID)ssj0001584282(PQKBManifestationID)16264136(PQKBTitleCode)TC0001584282(PQKBWorkID)14866067(PQKB)10225497(DE-He213)978-3-319-21852-6(MiAaPQ)EBC4178438(PPN)190519460(EXLCZ)99371000000047139320150903d2015 u| 0engur|n|---|||||txtccrMeasures of Complexity Festschrift for Alexey Chervonenkis /edited by Vladimir Vovk, Harris Papadopoulos, Alexander Gammerman1st ed. 2015.Cham :Springer International Publishing :Imprint: Springer,2015.1 online resource (413 p.)Description based upon print version of record.3-319-21851-4 Includes bibliographical references at the end of each chapters and index.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.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.Artificial intelligenceStatistics Mathematical statisticsMathematical optimizationArtificial Intelligencehttps://scigraph.springernature.com/ontologies/product-market-codes/I21000Statistical Theory and Methodshttps://scigraph.springernature.com/ontologies/product-market-codes/S11001Probability and Statistics in Computer Sciencehttps://scigraph.springernature.com/ontologies/product-market-codes/I17036Optimizationhttps://scigraph.springernature.com/ontologies/product-market-codes/M26008Artificial intelligence.Statistics .Mathematical statistics.Mathematical optimization.Artificial Intelligence.Statistical Theory and Methods.Probability and Statistics in Computer Science.Optimization.004Vovk Vladimiredthttp://id.loc.gov/vocabulary/relators/edtPapadopoulos Harrisedthttp://id.loc.gov/vocabulary/relators/edtGammerman Alexanderedthttp://id.loc.gov/vocabulary/relators/edtBOOK9910299209403321Measures of Complexity2509351UNINA