Algorithmic Learning in a Random World (Record no. 14314)

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 :
-- Springer US,
-- 2005.
300 ## - PHYSICAL DESCRIPTION
Extent XVI, 324 p.
Other physical details online resource.
336 ## -
-- text
-- txt
-- rdacontent
337 ## -
-- computer
-- c
-- rdamedia
338 ## -
-- online resource
-- cr
-- rdacarrier
347 ## -
-- text file
-- PDF
-- 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 ## -
-- ZDB-2-SCS
Holdings
Withdrawn status Lost status Source of classification or shelving scheme Damaged status Not for loan Home library Current library Date acquired Source of acquisition Total Checkouts Barcode Date last seen Price effective from Koha item type
    Dewey Decimal Classification     Central Library Central Library 28/06/2017 Springer EBook   E-37493 28/06/2017 28/06/2017 E-Book

Maintained by VTU Library