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Graphs for Pattern Recognition : Infeasible Systems of Linear Inequalities / Damir Gainanov.

By: Material type: TextTextLanguage: English Publisher: Berlin ; Boston : De Gruyter, [2016]Copyright date: ©2016Description: 1 online resource (158 p.)Content type:
  • text
Media type:
  • computer
Carrier type:
  • online resource
ISBN:
  • 9783110481068
Subject(s): Additional physical formats: No title; No titleDDC classification:
  • 510
Online resources:
Contents:
Frontmatter -- Preface -- Contents -- 1. Pattern recognition, infeasible systems of linear inequalities, and graphs -- 2. Complexes, (hyper)graphs, and inequality systems -- 3. Polytopes, positive bases, and inequality systems -- 4. Monotone Boolean functions, complexes, graphs, and inequality systems -- 5. Inequality systems, committees, (hyper)graphs, and alternative covers -- Bibliography -- List of notation -- Index
Title is part of eBook package: EBOOK PACKAGE COMPLETE 2016Title is part of eBook package: EBOOK PACKAGE Mathematics 2016Summary: 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. Contents:PrefacePattern recognition, infeasible systems of linear inequalities, and graphsInfeasible monotone systems of constraintsComplexes, (hyper)graphs, and inequality systemsPolytopes, positive bases, and inequality systemsMonotone Boolean functions, complexes, graphs, and inequality systemsInequality systems, committees, (hyper)graphs, and alternative coversBibliographyList of notationIndex
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Frontmatter -- Preface -- Contents -- 1. Pattern recognition, infeasible systems of linear inequalities, and graphs -- 2. Complexes, (hyper)graphs, and inequality systems -- 3. Polytopes, positive bases, and inequality systems -- 4. Monotone Boolean functions, complexes, graphs, and inequality systems -- 5. Inequality systems, committees, (hyper)graphs, and alternative covers -- Bibliography -- List of notation -- Index

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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. Contents:PrefacePattern recognition, infeasible systems of linear inequalities, and graphsInfeasible monotone systems of constraintsComplexes, (hyper)graphs, and inequality systemsPolytopes, positive bases, and inequality systemsMonotone Boolean functions, complexes, graphs, and inequality systemsInequality systems, committees, (hyper)graphs, and alternative coversBibliographyList of notationIndex

Mode of access: Internet via World Wide Web.

This eBook is made available Open Access. Unless otherwise specified in the content, the work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives (CC BY-NC-ND) license:

https://creativecommons.org/licenses/by-nc-nd/3.0

https://www.degruyter.com/dg/page/open-access-policy

In English.

Description based on online resource; title from PDF title page (publisher's Web site, viewed 26. Mrz 2019)

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