Nearest Neighbor Classifier with Margin Penalty for Active Learning

Cao, Yuan; Gao, Zhiqiao; Hu, Jie; Yang, Mingchuan

doi:10.1007/978-3-031-30105-6_32

Cited by 1 publication

(1 citation statement)

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“…Machine learning is often in this situation because of the size of its validation (Haalboom et al, 2022). In astronomy it is so necessary to match the training sample data (Carrick, 2022), but in machine learning so often there is a mismatch of training samples and in non-exclusive testing (Cao et al, 2022). The existence of and cutting into machine learning algorithms, missing data, censorship, and feature measurement errors are some of the challenges machine learning has in its use in astronomical statistics (Kim et al, 2022;Skaro, 2022).…”

Section: Ijccsmentioning

confidence: 99%

Statistical Data Retrieval Technique in Astronomy Computational Physics

Siagian

Pribadi²,

Sinaga

et al. 2023

JATISI

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Computational astronomy is a very important branch in today's era, where physicists or researchers can use computers to process statistics in astronomical physics. researchers can process abstract data from raw data and can convert data into data visualizations. Computational physics astronomy is a sophisticated and well-established method, this branch of science can provide and process data, solve complex problems, and is very helpful for statisticians and computer scientists. Astronomical physicists have many problems, among others; there is a problem that is hierarchical, and complex, so that this paper will provide a basis for methods for optimizing methods in processing statistical data on physics. The author's hope is that astronomical physicists can perform an important and effective processing of astronomical data optimally and effectively.

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Section: Ijccsmentioning

confidence: 99%