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Machine Learning Approaches to Star-...

Kim, Junhyung.

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Machine Learning Approaches to Star-Galaxy Classification.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning Approaches to Star-Galaxy Classification./
作者:	Kim, Junhyung.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:	156 p.
附註:	Source: Dissertation Abstracts International, Volume: 80-05(E), Section: B.
Contained By:	Dissertation Abstracts International80-05B(E).
標題:	Astronomy. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=13804762
ISBN:	9780438730809

Machine Learning Approaches to Star-Galaxy Classification.
Kim, Junhyung.

Machine Learning Approaches to Star-Galaxy Classification. - Ann Arbor : ProQuest Dissertations & Theses, 2018 - 156 p.

Source: Dissertation Abstracts International, Volume: 80-05(E), Section: B.

Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2018.

Accurate star-galaxy classification has many important applications in modern precision cosmology. However, a vast number of faint sources that are detected in the current and next-generation ground-based surveys may be challenged by poor star-galaxy classification. Thus, we explore a variety of machine learning approaches to improve star-galaxy classification in ground-based photometric surveys. In Chapter 2, we present a meta-classification framework that combines existing star-galaxy classifiers, and demonstrate that our Bayesian combination technique improves the overall performance over any individual classification method. In Chapter 3, we show that a deep learning algorithm called convolutional neural networks is able to produce accurate and well-calibrated classifications by learning directly from the pixel values of photometric images. In Chapter 4, we study another deep learning technique called generative adversarial networks in a semi-supervised setting, and demonstrate that our semi-supervised method produces competitive classifications using only a small amount of labeled examples.

ISBN: 9780438730809Subjects--Topical Terms:

517668
Astronomy.

Machine Learning Approaches to Star-Galaxy Classification.
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