MARC details
000 -LEADER |
fixed length control field |
03965nam a22005055i 4500 |
001 - CONTROL NUMBER |
control field |
978-0-387-25061-8 |
003 - CONTROL NUMBER IDENTIFIER |
control field |
DE-He213 |
005 - DATE AND TIME OF LATEST TRANSACTION |
control field |
20170628033240.0 |
007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION |
fixed length control field |
cr nn 008mamaa |
008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
fixed length control field |
100301s2005 xxu| s |||| 0|eng d |
020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
International Standard Book Number |
9780387250618 |
-- |
978-0-387-25061-8 |
024 7# - OTHER STANDARD IDENTIFIER |
Standard number or code |
10.1007/b106715 |
Source of number or code |
doi |
050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
Classification number |
Q334-342 |
050 #4 - LIBRARY OF CONGRESS CALL NUMBER |
Classification number |
TJ210.2-211.495 |
072 #7 - SUBJECT CATEGORY CODE |
Subject category code |
UYQ |
Source |
bicssc |
072 #7 - SUBJECT CATEGORY CODE |
Subject category code |
TJFM1 |
Source |
bicssc |
072 #7 - SUBJECT CATEGORY CODE |
Subject category code |
COM004000 |
Source |
bisacsh |
082 04 - DEWEY DECIMAL CLASSIFICATION NUMBER |
Classification number |
006.3 |
Edition number |
23 |
100 1# - MAIN ENTRY--PERSONAL NAME |
Personal name |
Vovk, Vladimir. |
Relator term |
author. |
245 10 - TITLE STATEMENT |
Title |
Algorithmic Learning in a Random World |
Medium |
[electronic resource] / |
Statement of responsibility, etc |
by Vladimir Vovk, Alexander Gammerman, Glenn Shafer. |
264 #1 - |
-- |
Boston, MA : |
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Springer US, |
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2005. |
300 ## - PHYSICAL DESCRIPTION |
Extent |
XVI, 324 p. |
Other physical details |
online resource. |
336 ## - |
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text |
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txt |
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rdacontent |
337 ## - |
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computer |
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c |
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rdamedia |
338 ## - |
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online resource |
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cr |
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rdacarrier |
347 ## - |
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text file |
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PDF |
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rda |
505 0# - FORMATTED CONTENTS NOTE |
Formatted contents note |
Conformal prediction -- Classification with conformal predictors -- Modifications of conformal predictors -- Probabilistic prediction I: impossibility results -- Probabilistic prediction II: Venn predictors -- Beyond exchangeability -- On-line compression modeling I: conformal prediction -- On-line compression modeling II: Venn prediction -- Perspectives and contrasts. |
520 ## - SUMMARY, ETC. |
Summary, etc |
Conformal prediction is a valuable new method of machine learning. Conformal predictors are among the most accurate methods of machine learning, and unlike other state-of-the-art methods, they provide information about their own accuracy and reliability. This new monograph integrates mathematical theory and revealing experimental work. It demonstrates mathematically the validity of the reliability claimed by conformal predictors when they are applied to independent and identically distributed data, and it confirms experimentally that the accuracy is sufficient for many practical problems. Later chapters generalize these results to models called repetitive structures, which originate in the algorithmic theory of randomness and statistical physics. The approach is flexible enough to incorporate most existing methods of machine learning, including newer methods such as boosting and support vector machines and older methods such as nearest neighbors and the bootstrap. Topics and Features: * Describes how conformal predictors yield accurate and reliable predictions, complemented with quantitative measures of their accuracy and reliability * Handles both classification and regression problems * Explains how to apply the new algorithms to real-world data sets * Demonstrates the infeasibility of some standard prediction tasks * Explains connections with Kolmogorov’s algorithmic randomness, recent work in machine learning, and older work in statistics * Develops new methods of probability forecasting and shows how to use them for prediction in causal networks Researchers in computer science, statistics, and artificial intelligence will find the book an authoritative and rigorous treatment of some of the most promising new developments in machine learning. Practitioners and students in all areas of research that use quantitative prediction or machine learning will learn about important new methods. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Computer science. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Data structures (Computer science). |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Artificial intelligence. |
650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Mathematical statistics. |
650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Computer Science. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Artificial Intelligence (incl. Robotics). |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Statistics and Computing/Statistics Programs. |
650 24 - SUBJECT ADDED ENTRY--TOPICAL TERM |
Topical term or geographic name as entry element |
Data Structures, Cryptology and Information Theory. |
700 1# - ADDED ENTRY--PERSONAL NAME |
Personal name |
Gammerman, Alexander. |
Relator term |
author. |
700 1# - ADDED ENTRY--PERSONAL NAME |
Personal name |
Shafer, Glenn. |
Relator term |
author. |
710 2# - ADDED ENTRY--CORPORATE NAME |
Corporate name or jurisdiction name as entry element |
SpringerLink (Online service) |
773 0# - HOST ITEM ENTRY |
Title |
Springer eBooks |
776 08 - ADDITIONAL PHYSICAL FORM ENTRY |
Display text |
Printed edition: |
International Standard Book Number |
9780387001524 |
856 40 - ELECTRONIC LOCATION AND ACCESS |
Uniform Resource Identifier |
<a href="http://dx.doi.org/10.1007/b106715">http://dx.doi.org/10.1007/b106715</a> |
912 ## - |
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ZDB-2-SCS |