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Machine Learning for Financial Engin...

Gyorfi, Laszlo.

Machine Learning for Financial Engineering[electronic resource].

纪录类型:	书目-语言数据,印刷品 : Monograph/item
[NT 15000414] null:	006.31
[NT 47271] Title/Author:	Machine Learning for Financial Engineering
作者:	Gyorfi, Laszlo.
[NT 51406] other author:	Ottucsak, Gyorgy.
出版者:	Singapore : : World Scientific,, 2012.
面页册数:	1 online resource (261 p.)
附注:	5.4. Universally Consistent Predictions: Unbounded Y.
标题:	Financial engineering - Data processing.
标题:	Machine learning.
标题:	Investments - Data processing.
ISBN:	9781848168145 (electronic bk.)
ISBN:	1848168144 (electronic bk.)
[NT 15000229] null:	This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment. The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and eng.
电子资源:	http://www.worldscientific.com/worldscibooks/10.1142/P818#t=toc

Machine Learning for Financial Engineering[electronic resource].
Gyorfi, Laszlo.

Machine Learning for Financial Engineering[electronic resource]. - Singapore :World Scientific,2012. - 1 online resource (261 p.) - Advances in Computer Science and Engineering: Texts. - Advances in computer science and engineering.Texts..

5.4. Universally Consistent Predictions: Unbounded Y.

This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment. The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and eng.

ISBN: 9781848168145 (electronic bk.)Subjects--Topical Terms:

556766
Financial engineering
--Data processing.Index Terms--Genre/Form:

336502
Electronic books.

LC Class. No.: Q325.5 .M321 2012

Dewey Class. No.: 006.31

Machine Learning for Financial Engineering[electronic resource].
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082 0 4 $a 006.31
100 1 $a Gyorfi, Laszlo. $3 556762
245 1 0 $a Machine Learning for Financial Engineering $h [electronic resource].
260 $a Singapore : $b World Scientific, $c 2012.
300 $a 1 online resource (261 p.)
490 1 $a Advances in Computer Science and Engineering: Texts
500 $a 5.4. Universally Consistent Predictions: Unbounded Y.
520 $a This volume investigates algorithmic methods based on machine learning in order to design sequential investment strategies for financial markets. Such sequential investment strategies use information collected from the market's past and determine, at the beginning of a trading period, a portfolio; that is, a way to invest the currently available capital among the assets that are available for purchase or investment. The aim is to produce a self-contained text intended for a wide audience, including researchers and graduate students in computer science, finance, statistics, mathematics, and eng.
588 $a Description based on print version record.
650 0 $a Financial engineering $x Data processing. $3 556766
650 0 $a Machine learning. $3 202931
650 0 $a Investments $x Data processing. $3 556767
655 0 $a Electronic books. $2 local $3 336502
700 1 $a Ottucsak, Gyorgy. $3 556763
700 1 $a Walk, Harro. $3 556764
830 0 $a Advances in computer science and engineering. $p Texts. $3 556765
856 4 0 $u http://www.worldscientific.com/worldscibooks/10.1142/P818#t=toc

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