Advances in Machine Learning and Data Mining for Astronomy (Hardcover)
Michael J. Way, Jeffrey D. Scargle, Kamal M. Ali, Ashok N. Srivastava
- 出版商: CRC
- 出版日期: 2012-03-29
- 售價: $6,700
- 貴賓價: 9.5 折 $6,365
- 語言: 英文
- 頁數: 744
- 裝訂: Hardcover
- ISBN: 143984173X
- ISBN-13: 9781439841730
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相關分類:
Machine Learning、Data-mining
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相關主題
商品描述
Advances in Machine Learning and Data Mining for Astronomy documents numerous successful collaborations among computer scientists, statisticians, and astronomers who illustrate the application of state-of-the-art machine learning and data mining techniques in astronomy. Due to the massive amount and complexity of data in most scientific disciplines, the material discussed in this text transcends traditional boundaries between various areas in the sciences and computer science.
The book’s introductory part provides context to issues in the astronomical sciences that are also important to health, social, and physical sciences, particularly probabilistic and statistical aspects of classification and cluster analysis. The next part describes a number of astrophysics case studies that leverage a range of machine learning and data mining technologies. In the last part, developers of algorithms and practitioners of machine learning and data mining show how these tools and techniques are used in astronomical applications.
With contributions from leading astronomers and computer scientists, this book is a practical guide to many of the most important developments in machine learning, data mining, and statistics. It explores how these advances can solve current and future problems in astronomy and looks at how they could lead to the creation of entirely new algorithms within the data mining community.
商品描述(中文翻譯)
《機器學習和數據挖掘在天文學中的進展》記錄了計算機科學家、統計學家和天文學家之間的許多成功合作案例,展示了最先進的機器學習和數據挖掘技術在天文學中的應用。由於大多數科學領域中數據的龐大量和複雜性,本書所討論的內容超越了科學和計算機科學之間的傳統界限。
本書的引言部分提供了天文學科中與健康、社會和物理科學相關的背景,特別是關於分類和聚類分析的概率和統計方面的問題。接下來的部分描述了一些天體物理學案例研究,利用了各種機器學習和數據挖掘技術。在最後一部分中,算法開發人員和機器學習、數據挖掘實踐者展示了這些工具和技術在天文學應用中的使用方式。
本書由領先的天文學家和計算機科學家共同貢獻,是機器學習、數據挖掘和統計學中許多重要發展的實用指南。它探討了這些進展如何解決當前和未來的天文學問題,並探討了它們如何可能在數據挖掘社區中引發全新算法的創造。