東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Sets as Measures: Optimization and M...

Boyd, Nicholas.

Linked to FindBook

Google Book

Amazon

博客來

Sets as Measures: Optimization and Machine Learning.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Sets as Measures: Optimization and Machine Learning./
Author:	Boyd, Nicholas.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2018,
Description:	98 p.
Notes:	Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
Contained By:	Dissertations Abstracts International80-03B.
Subject:	Applied Mathematics. -
Online resource:	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.
LDR:01865nmm a2200349 4500 001 2208974
005 20191025102624.5
008 201008s2018 ||||||||||||||||| ||eng d
020 $a 9780438324633
035 $a (MiAaPQ)AAI10816068
035 $a (MiAaPQ)berkeley:17836
035 $a AAI10816068
040 $a MiAaPQ $c MiAaPQ
100 1 $a Boyd, Nicholas. $3 3436052
245 1 0 $a Sets as Measures: Optimization and Machine Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2018
300 $a 98 p.
500 $a Source: Dissertations Abstracts International, Volume: 80-03, Section: B.
500 $a Publisher info.: Dissertation/Thesis.
500 $a Advisor: Jordan, Michael;Recht, Benjamin.
502 $a Thesis (Ph.D.)--University of California, Berkeley, 2018.
506 $a This item must not be added to any third party search indexes.
506 $a This item must not be sold to any third party vendors.
520 $a 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.
590 $a School code: 0028.
650 4 $a Applied Mathematics. $3 1669109
650 4 $a Statistics. $3 517247
650 4 $a Artificial intelligence. $3 516317
690 $a 0364
690 $a 0463
690 $a 0800
710 2 $a University of California, Berkeley. $b Statistics. $3 1676555
773 0 $t Dissertations Abstracts International $g 80-03B.
790 $a 0028
791 $a Ph.D.
792 $a 2018
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10816068