Graphs for Pattern Recognition : Infeasible Systems of Linear Inequalities (Record no. 37145)

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001 - CONTROL NUMBER
control field https://directory.doabooks.org/handle/20.500.12854/30837
005 - DATE AND TIME OF LATEST TRANSACTION
control field 20220219190121.0
020 ## - INTERNATIONAL STANDARD BOOK NUMBER
International Standard Book Number /doi.org/10.1515/9783110481068
024 7# - OTHER STANDARD IDENTIFIER
Standard number or code https://doi.org/10.1515/9783110481068
Terms of availability doi
041 0# - LANGUAGE CODE
Language code of text/sound track or separate title English
042 ## - AUTHENTICATION CODE
Authentication code dc
072 #7 - SUBJECT CATEGORY CODE
Subject category code UYQV
Source bicssc
072 #7 - SUBJECT CATEGORY CODE
Subject category code TV
Source bicssc
100 1# - MAIN ENTRY--PERSONAL NAME
Personal name Gainanov, Damir
Relationship auth
245 10 - TITLE STATEMENT
Title Graphs for Pattern Recognition : Infeasible Systems of Linear Inequalities
260 ## - PUBLICATION, DISTRIBUTION, ETC.
Name of publisher, distributor, etc. De Gruyter
Date of publication, distribution, etc. 2016
506 0# - RESTRICTIONS ON ACCESS NOTE
Terms governing access Open Access
Source of term star
Standardized terminology for access restriction Unrestricted online access
520 ## - SUMMARY, ETC.
Summary, etc. This monograph deals with mathematical constructions that are foundational in such an important area of data mining as pattern recognition. By using combinatorial and graph theoretic techniques, a closer look is taken at infeasible systems of linear inequalities, whose generalized solutions act as building blocks of geometric decision rules for pattern recognition. Infeasible systems of linear inequalities prove to be a key object in pattern recognition problems described in geometric terms thanks to the committee method. Such infeasible systems of inequalities represent an important special subclass of infeasible systems of constraints with a monotonicity property – systems whose multi-indices of feasible subsystems form abstract simplicial complexes (independence systems), which are fundamental objects of combinatorial topology. The methods of data mining and machine learning discussed in this monograph form the foundation of technologies like big data and deep learning, which play a growing role in many areas of human-technology interaction and help to find solutions, better solutions and excellent solutions.
536 ## - FUNDING INFORMATION NOTE
Text of note Knowledge Unlatched
540 ## - TERMS GOVERNING USE AND REPRODUCTION NOTE
Terms governing use and reproduction Creative Commons
Use and reproduction rights https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
Source of term cc
-- https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode
546 ## - LANGUAGE NOTE
Language note English
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Computer vision
Source of heading or term bicssc
650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM
Topical term or geographic name entry element Agriculture & farming
Source of heading or term bicssc
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term Computers
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term Artificial Intelligence
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term Computer Vision & Pattern Recognition
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term Technology & Engineering
653 ## - INDEX TERM--UNCONTROLLED
Uncontrolled term Agriculture
856 40 - ELECTRONIC LOCATION AND ACCESS
Host name www.oapen.org
Uniform Resource Identifier <a href="https://library.oapen.org/bitstream/20.500.12657/46036/1/external_content.pdf">https://library.oapen.org/bitstream/20.500.12657/46036/1/external_content.pdf</a>
Access status 0
Public note DOAB: download the publication
856 40 - ELECTRONIC LOCATION AND ACCESS
Host name www.oapen.org
Uniform Resource Identifier <a href="https://directory.doabooks.org/handle/20.500.12854/30837">https://directory.doabooks.org/handle/20.500.12854/30837</a>
Access status 0
Public note DOAB: description of the publication

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