THE 80/20 RULE: LIBRARY LORE OR STATISTICAL LAW?

1985 ◽  
Vol 41 (1) ◽  
pp. 24-39 ◽  
Author(s):  
QUENTIN L. BURRELL
Keyword(s):  
Rule Library

scholarly journals BCSExplorer: a customized biosynthetic chemical space explorer with multifunctional objective function analysis

2019 ◽  
Author(s):  
Yu Tian ◽  
Ling Wu ◽  
Le Yuan ◽  
Shaozhen Ding ◽  
Fu Chen ◽  
...  
Keyword(s):  
Chemical Space ◽  
Function Analysis ◽  
Supplementary Information ◽  
Bulk Chemicals ◽  
Toxicity Analysis ◽  
Novel Method ◽  
Living Organisms ◽  
Almost All ◽  
New Generation ◽  
Rule Library

Abstract Summary The biosynthetic ability of living organisms has important applications in producing bulk chemicals, biofuels and natural products. Based on the most comprehensive biosynthesis knowledgebase, a computational system, BCSExplorer, is proposed to discover the unexplored chemical space using nature’s biosynthetic potential. BCSExplorer first integrates the most comprehensive biosynthetic reaction database with 280 000 biochemical reactions and 60 000 chemicals biosynthesized globally over the past 130 years. Second, in this study, a biosynthesis tree is computed for a starting chemical molecule based on a comprehensive biotransformation rule library covering almost all biosynthetic possibilities, in which redundant rules are removed using a new algorithm. Moreover, biosynthesis feasibility, drug-likeness and toxicity analysis of a new generation of compounds will be pursued in further studies to meet various needs. BCSExplorer represents a novel method to explore biosynthetically available chemical space. Availability and implementation BCSExplorer is available at: http://www.rxnfinder.org/bcsexplorer/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Jamming decision under condition of incomplete jamming rule library

2019 ◽  
Vol 2019 (21) ◽  
pp. 7449-7454
Author(s):  
Qiang Xing ◽  
Weigang Zhu ◽  
Zhou Chi ◽  
Guangyong Zheng
Keyword(s):  
Rule Library

scholarly journals REMOVING LAND COVER SPURIOUS CHANGE BY GEO-ECO ZONING RULE BASE

2020 ◽  
Vol XLII-3/W10 ◽  
pp. 677-683
Author(s):  
L. Zhu ◽  
Y. X. La ◽  
R. M. Shi ◽  
S. Peng
Keyword(s):  
Remote Sensing ◽  
Land Cover ◽  
Change Detection ◽  
Spectral Characteristics ◽  
Actual State ◽  
Rule Base ◽  
Visual Interpretation ◽  
Base Management System ◽  
Base Management ◽  
Rule Library

Abstract. The speed of change in land cover is growing faster with the development of social science and technology. Remote sensing has become the most effective way to monitor change information. However, remote sensing images reflect only the instantaneous state of the Earth’s surface. Spectral characteristics cannot correctly reflect the actual state, and this inability results in the limited classification accuracy of land cover products. In order to obtain high accuracy change detection results, it is necessary to identify and eliminate spurious changes.At present, the spurious changes are generally identified by visual interpretation which not only labor and time consuming, but also easily lead to misjudgment due to the lack of identification experience of the interpreter. Therefore, it is urgent to establish a spurious change rule base to automatically identify spurious changes. In this study, the global geo-eco zoning can be used to build a rule base to identify and eliminate spurious changes.The structure and content of the rule base are designed, the rules are represented and put into the rule library, the plugins are designed to remove spurious changes, and a rule base management system is established to identify the spurious changes using the rules in the rule base. 30m Land cover products of Laos were selected as the experimental area to verify the accuracy of the change patches after eliminating spurious changes. Results show that the accuracy of change detection is improved by using the rule base of geo-eco zoning to identify spurious changes.

scholarly journals Neighborhood Granule Classifiers

2018 ◽  
Vol 8 (12) ◽  
pp. 2646 ◽  
Author(s):  
Hongbo Jiang ◽  
Yumin Chen
Keyword(s):  
Neural Networks ◽  
Rough Set ◽  
Classification Performance ◽  
Granular Structure ◽  
Classification Systems ◽  
Similarity Metrics ◽  
Linear Classifiers ◽  
Neighborhood Rough Set ◽  
Linear And Nonlinear ◽  
Rule Library

Classifiers are divided into linear and nonlinear classifiers. The linear classifiers are built on a basis of some hyper planes. The nonlinear classifiers are mainly neural networks. In this paper, we propose a novel neighborhood granule classifier based on a concept of granular structure and neighborhood granules of datasets. By introducing a neighborhood rough set model, the condition features and decision features of classification systems are respectively granulated to form some condition neighborhood granules and decision neighborhood granules. These neighborhood granules are sets; thus, their calculations are intersection and union operations of sets. A condition neighborhood granule and a decision neighborhood granule form a granular rule, and the collection of granular rules constitutes a granular rule library. Furthermore, we propose two kinds of distance and similarity metrics to measure granules, which are used for the searching and matching of granules. Thus, we design a granule classifier by the similarity metric. Finally, we use the granule classifier proposed in this paper for a classification test with UCI datasets. The theoretical analysis and experiments show that the proposed granule classifier achieves a better classification performance under an appropriate neighborhood granulation parameter.

The construction of Chinese grammar proofreading XML rule library

2012 ◽  
Author(s):  
Ying Jiang ◽  
Xiaohong Zeng ◽  
Weihao Lin ◽  
Shaoying Lin ◽  
Jingfeng Hu ◽  
...  
Keyword(s):  
Rule Library

A high-level design rule library addressing CMOS and heterogeneous technologies

2014 ◽  
Author(s):  
Gerald Cibrario ◽  
Marjorie Gary ◽  
Fabien Gays ◽  
Karim Azizi-Mourier ◽  
Olivier Billoint ◽  
...  
Keyword(s):  
Design Rule ◽  
Heterogeneous Technologies ◽  
Level Design ◽  
High Level ◽  
Rule Library
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