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中图分类法:
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TP311.13 版次： |
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著者:
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Wright, Stephen J., |
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题名:
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Optimization for data analysis / / , |
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出版发行:
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出版地： Cambridge, United Kingdom : 出版社： Cambridge University Press, 出版日期： 2022. |
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载体形态:
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x, 227 pages : illustrations ; 24 cm |
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内容提要:
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"Optimization formulations and algorithms have long played a central role in data analysis and machine learning. Maximum likelihood concepts date to Gauss and Laplace in the late 1700s ; problems of this type drove developments in unconstrained optimization in the latter half of the 20th century. Mangasarian's papers in the 1960s on pattern separation using linear programming made an explicit connection between machine learning and optimization in the early days of the former subject. During the 1990s, optimization techniques (especially quadratic programming and duality) were key to the development of support vector machines and kernel learning. The period 1997-2010 saw many synergies emerge between regularized / sparse optimization, variable selection, and compressed sensing. In the current era of deep learning, two optimization techniques-stochastic gradient and automatic differentiation (a.k.a. back-propagation)-are essential."--Provided by publisher. |
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主题词:
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Big data. |
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主题词:
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Mathematical optimization. |
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主题词:
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Quantitative research. |
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主题词:
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Artificial intelligence. |
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主要责任者:
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Recht, Benjamin, Recht, Benjamin, |