Reconstructability analysis : theory and applications.
Title:
Reconstructability analysis : theory and applications.
Author:
Zwick, Martin.
ISBN:
9781845443917
Personal Author:
Physical Description:
1 online resource (212 pages)
Contents:
CONTENTS -- EDITORIAL ADVISORY BOARD -- Abstracts and keywords -- Preface -- Editorial -- An overview of reconstructability analysis -- Modified reconstructability analysis for many-valued functions and relations -- Reversible modified reconstructability analysis of Boolean circuits and its quantum computation -- A comparison of modified reconstructability analysis and Ashenhurst-Curtis decomposition of Boolean functions -- Multi-level decomposition of probabilistic relations -- The k-systems glitch: granulation of predictor variables -- Directed extended dependency analysis for data mining -- Instant modelling and data-knowledge processing by reconstructability analysis -- Application of reconstructability analysis in system structure -- A software architecture for reconstructability analysis -- Forecast entropy -- The forecast model of system reconstructability analysis -- Construction of main sequence of gene based on "method of factor reconstruction analysis" -- Reconstructability analysis with Fourier transforms -- State-based reconstructability analysis -- Reconstructability analysis detection of optimal gene order in genetic algorithms -- Book reviews -- Book reports -- Announcements -- Special announcements.
Abstract:
A novel many-valued decomposition within the framework of lossless reconstructability analysis (RA) is presented. In previous work, modified reconstructability analysis (MRA) was applied to Boolean functions, where it was shown that most Boolean functions not decomposable using conventional reconstructability analysis (CRA) are decomposable using MRA. Also, it was previously shown that whenever decomposition exists in both MRA and CRA, MRA yields simpler or equal complexity decompositions. In this paper, MRA is extended to many-valued logic functions, and logic structures that correspond to such decomposition are developed. It is shown that many-valued MRA can decompose manyvalued functions when CRA fails to do so. Since real-life data are often many-valued, this new decomposition can be useful for machine learning and data mining. Many-valued MRA can also be applied for the decomposition of relations.
Local Note:
Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2017. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries.
Genre:
Electronic Access:
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