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Machine Learning for the Sciences.

ProQuest Information and Learning Co.

Machine Learning for the Sciences.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Machine Learning for the Sciences./
作者:	Bache, Kevin M.
面頁冊數:	1 online resource (88 pages)
附註:	Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
Contained By:	Dissertation Abstracts International79-04B(E).
標題:	Computer science. -
電子資源:	click for full text (PQDT)
ISBN:	9780355413960

Machine Learning for the Sciences.
Bache, Kevin M.

Machine Learning for the Sciences. - 1 online resource (88 pages)

Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.

Thesis (Ph.D.)

Includes bibliographical references

Progress in the sciences depends critically on the analysis of ever-growing bodies of data. Many of these analysis patterns are inferential in nature; their goal is to infer the value of one or more parameters which bear some real-world meaning. Others are in essence discriminative; their goal is to build a black-box model with the strongest possible predictive power. For both of these analysis styles, machine learning offers a host of powerful tools to tackle historically unapproachable problems.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355413960Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Machine Learning for the Sciences.
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020 $a 9780355413960
035 $a (MiAaPQ)AAI10634466
035 $a (MiAaPQ)uci:14776
035 $a AAI10634466
040 $a MiAaPQ $b eng $c MiAaPQ
099 $a TUL $f hyy $c available through World Wide Web
100 1 $a Bache, Kevin M. $3 1179554
245 1 0 $a Machine Learning for the Sciences.
264 0 $c 2017
300 $a 1 online resource (88 pages)
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
500 $a Source: Dissertation Abstracts International, Volume: 79-04(E), Section: B.
500 $a Adviser: Pierre Baldi.
502 $a Thesis (Ph.D.) $c University of California, Irvine $d 2017.
504 $a Includes bibliographical references
520 $a Progress in the sciences depends critically on the analysis of ever-growing bodies of data. Many of these analysis patterns are inferential in nature; their goal is to infer the value of one or more parameters which bear some real-world meaning. Others are in essence discriminative; their goal is to build a black-box model with the strongest possible predictive power. For both of these analysis styles, machine learning offers a host of powerful tools to tackle historically unapproachable problems.
520 $a In this dissertation, I present three examples of machine learning tools applied to the sciences. The first offers a novel model of textual diversity applied to the science of science itself. The second, explores a series of discriminative models which probe the evolution of the cosmos. The third offers a novel convolutional neural architecture for discriminating effective from ineffective drug candidates.
520 $a Taken together, these studies offer a glimpse of the breadth and potency of the contributions that machine learning can offer to the sciences.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
650 4 $a Statistics. $3 556824
650 4 $a Artificial intelligence. $3 559380
650 4 $a Information science. $3 561178
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
690 $a 0463
690 $a 0800
690 $a 0723
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Irvine. $b Computer Science. $3 1179555
773 0 $t Dissertation Abstracts International $g 79-04B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10634466 $z click for full text (PQDT)

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