國立虎尾科技大學 |

Measures of complexity = festschrift for Alexey Chervonenkis /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Measures of complexity/ edited by Vladimir Vovk, Harris Papadopoulos, Alexander Gammerman.
Reminder of title:	festschrift for Alexey Chervonenkis /
other author:	Gammerman, Alexander.
Published:	Cham :Imprint: Springer, : 2015.,
Description:	xxxi, 399 p. :ill., digital ; : 24 cm.;
Contained By:	Springer eBooks
Subject:	Optimization. -
Online resource:	http://dx.doi.org/10.1007/978-3-319-21852-6
ISBN:	9783319218526

Measures of complexity = festschrift for Alexey Chervonenkis /
Measures of complexityfestschrift for Alexey Chervonenkis /[electronic resource] :edited by Vladimir Vovk, Harris Papadopoulos, Alexander Gammerman. - Cham :Imprint: Springer,2015. - xxxi, 399 p. :ill., digital ;24 cm.

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.

ISBN: 9783319218526

Standard No.: 10.1007/978-3-319-21852-6doiSubjects--Topical Terms:

669174
Optimization.

LC Class. No.: Q325.5

Dewey Class. No.: 006.31

Measures of complexity = festschrift for Alexey Chervonenkis /
LDR:03290nam a2200325 a 4500 001 838164
003 DE-He213
005 20160422160524.0
006 m d
007 cr nn 008maaau
008 160616s2015 gw s 0 eng d
020 $a 9783319218526 $q (electronic bk.)
020 $a 9783319218519 $q (paper)
024 7 $a 10.1007/978-3-319-21852-6 $2 doi
035 $a 978-3-319-21852-6
040 $a GP $c GP
041 0 $a eng
050 4 $a Q325.5
072 7 $a UYQ $2 bicssc
072 7 $a TJFM1 $2 bicssc
072 7 $a COM004000 $2 bisacsh
082 0 4 $a 006.31 $2 23
090 $a Q325.5 $b .M484 2015
245 0 0 $a Measures of complexity $h [electronic resource] : $b festschrift for Alexey Chervonenkis / $c edited by Vladimir Vovk, Harris Papadopoulos, Alexander Gammerman.
260 $a Cham : $c 2015. $b Imprint: Springer, $b Springer International Publishing :
300 $a xxxi, 399 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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.
520 $a 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.
650 2 4 $a Optimization. $3 669174
650 2 4 $a Probability and Statistics in Computer Science. $3 669886
650 2 4 $a Statistical Theory and Methods. $3 671396
650 2 4 $a Artificial Intelligence (incl. Robotics) $3 593924
650 1 4 $a Computer Science. $3 593922
650 0 $a Pattern recognition systems. $3 557384
650 0 $a Machine learning. $3 561253
700 1 $a Gammerman, Alexander. $3 669776
700 1 $a Papadopoulos, Harris. $3 815994
700 1 $a Vovk, Vladimir. $3 669774
700 1 $a Chervonenkis, Alexey. $3 1069161
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer eBooks
856 4 0 $u http://dx.doi.org/10.1007/978-3-319-21852-6
950 $a Computer Science (Springer-11645)