Generative Models in the Problem of Evaluating the Efficiency of Computer Algorithms
The author formulates definitions of computer algorithm efficiency according to a criterion that characterizes accuracy, reliability, performance speed, and other consumer properties. Schemes of proof experiments based on stochastic models of generating artificial data with statistical characteristi...
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| Published in | Cybernetics and systems analysis Vol. 60; no. 2; pp. 320 - 330 |
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| Main Author | |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.03.2024
Springer Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 1060-0396 1573-8337 |
| DOI | 10.1007/s10559-024-00672-9 |
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| Abstract | The author formulates definitions of computer algorithm efficiency according to a criterion that characterizes accuracy, reliability, performance speed, and other consumer properties. Schemes of proof experiments based on stochastic models of generating artificial data with statistical characteristics adequate to real observations are suggested. The experiments are aimed at determining the efficiency of computer algorithms that provide solutions to three different problems, namely, the optimal stopping for making a final decision during a sequential analysis of alternatives, training a linear classifier based on a finite sample of observations, and determining diagnostic signs of an ECG using the fasegraphy method. The results obtained based on statistical experiments are given. |
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| AbstractList | The author formulates definitions of computer algorithm efficiency according to a criterion that characterizes accuracy, reliability, performance speed, and other consumer properties. Schemes of proof experiments based on stochastic models of generating artificial data with statistical characteristics adequate to real observations are suggested. The experiments are aimed at determining the efficiency of computer algorithms that provide solutions to three different problems, namely, the optimal stopping for making a final decision during a sequential analysis of alternatives, training a linear classifier based on a finite sample of observations, and determining diagnostic signs of an ECG using the fasegraphy method. The results obtained based on statistical experiments are given. Keywords: computer algorithm efficiency, statistical experiment, artificial data generating. The author formulates definitions of computer algorithm efficiency according to a criterion that characterizes accuracy, reliability, performance speed, and other consumer properties. Schemes of proof experiments based on stochastic models of generating artificial data with statistical characteristics adequate to real observations are suggested. The experiments are aimed at determining the efficiency of computer algorithms that provide solutions to three different problems, namely, the optimal stopping for making a final decision during a sequential analysis of alternatives, training a linear classifier based on a finite sample of observations, and determining diagnostic signs of an ECG using the fasegraphy method. The results obtained based on statistical experiments are given. |
| Audience | Academic |
| Author | Fainzilberg, L. S. |
| Author_xml | – sequence: 1 givenname: L. S. surname: Fainzilberg fullname: Fainzilberg, L. S. email: fainzilberg@gmail.com organization: International Research and Training Center for Information Technologies and Systems, National Academy of Sciences of Ukraine and Ministry of Education and Science of Ukraine, National Technical University of Ukraine “Igor Sikorsky Kyiv Polytechnic Institute” |
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| Cites_doi | 10.1007/s10559-023-00578-y 10.1016/j.bspc.2018.03.003 10.1080/00207543.2016.1192301 10.1214/ss/1177012493 10.5539/ibr.v5n1p61 10.1007/978-1-4757-4145-2 10.1007/s10559-020-00283-0 10.1016/0898-1221(90)90145-A 10.1080/01421590802572791 10.1002/9781118631980 10.1016/B978-012369378-5/50022-3 10.1016/j.ejor.2003.10.048 10.1007/978-0-387-30164-8_18 10.1007/978-94-017-3217-8 10.1145/3017680.3017698 10.15407/kvt192.02 10.1615/JAutomatInfScien.v43.i9.20 10.1088/1742-6596/892/1/012016 10.15407/kvt199.01.059 |
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| References | Robert, Casella (CR16) 2004 Fainzilberg (CR20) 2020; 56 CR2 Cormen, Leiserson, Rivest, Stein (CR3) 2022 Sakaguchi (CR9) 1976; 21 CR4 CR6 Beckman (CR12) 1990; 19 CR5 Li, Wen, Chen (CR8) 2016; 54 CR7 CR18 CR17 CR14 Rubinstein, Kroese (CR15) 2016 Berkaya, Uysal, Gunal, Ergin, Gulmezoglu (CR19) 2018; 43 CR13 CR23 CR11 Ferguson (CR10) 1989; 4 Meats, Heneghan, Crilly, Glasziou (CR21) 2009; 31 Wahab, Rose, Osman (CR1) 2012; 5 Zhukovska, Fainzilberg (CR22) 2023; 59 672_CR4 DC Li (672_CR8) 2016; 54 672_CR17 672_CR2 672_CR14 CP Robert (672_CR16) 2004 TS Ferguson (672_CR10) 1989; 4 672_CR18 OA Zhukovska (672_CR22) 2023; 59 672_CR7 SA Wahab (672_CR1) 2012; 5 672_CR6 RY Rubinstein (672_CR15) 2016 672_CR5 M Sakaguchi (672_CR9) 1976; 21 TH Cormen (672_CR3) 2022 SK Berkaya (672_CR19) 2018; 43 E Meats (672_CR21) 2009; 31 LS Fainzilberg (672_CR20) 2020; 56 MJ Beckman (672_CR12) 1990; 19 672_CR23 672_CR13 672_CR11 |
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| SubjectTerms | Algorithms Artificial Intelligence Computer Engineering Computing time Control Decision analysis Electrocardiogram Electrocardiography Informatics Mathematics Mathematics and Statistics New Means of Cybernetics Processor Architectures Sequential analysis Software Engineering/Programming and Operating Systems Stochastic models Systems Analysis Systems Theory |
| Title | Generative Models in the Problem of Evaluating the Efficiency of Computer Algorithms |
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