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Sets as Measures: Optimization and M...
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Boyd, Nicholas.
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Sets as Measures: Optimization and Machine Learning.
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Sets as Measures: Optimization and Machine Learning./
作者:
Boyd, Nicholas.
出版者:
Ann Arbor : ProQuest Dissertations & Theses, : 2018,
面頁冊數:
98 p.
附註:
Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
Contained By:
Dissertations Abstracts International80-03B.
標題:
Applied Mathematics. -
電子資源:
http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10816068
ISBN:
9780438324633
Sets as Measures: Optimization and Machine Learning.
Boyd, Nicholas.
Sets as Measures: Optimization and Machine Learning.
- Ann Arbor : ProQuest Dissertations & Theses, 2018 - 98 p.
Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
Thesis (Ph.D.)--University of California, Berkeley, 2018.
This item must not be added to any third party search indexes.
The purpose of this thesis is to address the following simple question: How do we design efficient algorithms to solve optimization or machine learning problems where the decision variable (or target label) is a set of unknown cardinality? In this thesis we show that, in some cases, optimization and machine learning algorithms designed to work with single vectors can be directly applied to problems involving sets. We do this by invoking a classical trick: we lift sets to elements of a vector space, namely an infinite-dimensional space of measures. While this idea has been explored extensively in theoretical analysis, we show that it also generates efficient practical algorithms.
ISBN: 9780438324633Subjects--Topical Terms:
1669109
Applied Mathematics.
Sets as Measures: Optimization and Machine Learning.
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