scholarly journals Compression and Classification Methods for Galaxy Spectra in Large Redshift Surveys

2006 ◽  
pp. 33-52 ◽  
Author(s):  
Ofer Lahav
Keyword(s):  
Classification Methods ◽  
Large Redshift ◽  
Redshift Surveys

An Inversion Method for Measuring β in Large‐Redshift Surveys

2002 ◽  
Vol 579 (1) ◽  
pp. 76-82 ◽  
Author(s):  
Stephen D. Landy ◽  
Alexander S. Szalay
Keyword(s):  
Inversion Method ◽  
Large Redshift ◽  
Redshift Surveys

ON THE USE OF MULTICRITERIA CLASSIFICATION METHODS: A SIMULATION STUDY

2001 ◽  
Vol 06 (02) ◽  
Author(s):  
Michael Doumpos ◽  
Constantin Zopounidis
Keyword(s):  
Simulation Study ◽  
Classification Methods

Review of classification studies for machine learning in the development of intelligent management decision support systems

2020 ◽  
Vol 89 ◽  
pp. 20-29
Author(s):  
Sh. K. Kadiev ◽  
◽  
R. Sh. Khabibulin ◽  
P. P. Godlevskiy ◽  
V. L. Semikov ◽  
...  
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Mathematical Models ◽  
Decision Support Systems ◽  
Support Systems ◽  
Management Decision ◽  
Classification Methods ◽  
Advantages And Disadvantages ◽  
Intelligent Management ◽  
Management Decision Support

Introduction. An overview of research in the field of classification as a method of machine learning is given. Articles containing mathematical models and algorithms for classification were selected. The use of classification in intelligent management decision support systems in various subject areas is also relevant. Goal and objectives. The purpose of the study is to analyze papers on the classification as a machine learning method. To achieve the objective, it is necessary to solve the following tasks: 1) to identify the most used classification methods in machine learning; 2) to highlight the advantages and disadvantages of each of the selected methods; 3) to analyze the possibility of using classification methods in intelligent systems to support management decisions to solve issues of forecasting, prevention and elimination of emergencies. Methods. To obtain the results, general scientific and special methods of scientific knowledge were used - analysis, synthesis, generalization, as well as the classification method. Results and discussion thereof. According to the results of the analysis, studies with a mathematical formulation and the availability of software developments were identified. The issues of classification in the implementation of machine learning in the development of intelligent decision support systems are considered. Conclusion. The analysis revealed that enough algorithms were used to perform the classification while sorting the acquired knowledge within the subject area. The implementation of an accurate classification is one of the fundamental problems in the development of management decision support systems, including for fire and emergency prevention and response. Timely and effective decision by officials of operational shifts for the disaster management is also relevant. Key words: decision support, analysis, classification, machine learning, algorithm, mathematical models.

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