國立虎尾科技大學 |

Nonparametric Kernel Density Estimation and Its Computational Aspects

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Nonparametric Kernel Density Estimation and Its Computational Aspects/ by Artur Gramacki.
作者:	Gramacki, Artur.
面頁冊數:	XXIX, 176 p. 70 illus.online resource. :
Contained By:	Springer Nature eBook
標題:	Computational intelligence. -
電子資源:	https://doi.org/10.1007/978-3-319-71688-6
ISBN:	9783319716886

Nonparametric Kernel Density Estimation and Its Computational Aspects
Gramacki, Artur.

Nonparametric Kernel Density Estimation and Its Computational Aspects[electronic resource] /by Artur Gramacki. - 1st ed. 2018. - XXIX, 176 p. 70 illus.online resource. - Studies in Big Data,372197-6503 ;. - Studies in Big Data,8.

This book describes computational problems related to kernel density estimation (KDE) – one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.

ISBN: 9783319716886

Standard No.: 10.1007/978-3-319-71688-6doiSubjects--Topical Terms:

568984
Computational intelligence.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Nonparametric Kernel Density Estimation and Its Computational Aspects
LDR:02345nam a22003975i 4500 001 989456
003 DE-He213
005 20200703154700.0
007 cr nn 008mamaa
008 201225s2018 gw | s |||| 0|eng d
020 $a 9783319716886 $9 978-3-319-71688-6
024 7 $a 10.1007/978-3-319-71688-6 $2 doi
035 $a 978-3-319-71688-6
050 4 $a Q342
072 7 $a UYQ $2 bicssc
072 7 $a TEC009000 $2 bisacsh
072 7 $a UYQ $2 thema
082 0 4 $a 006.3 $2 23
100 1 $a Gramacki, Artur. $e author. $4 aut $4 http://id.loc.gov/vocabulary/relators/aut $3 1281326
245 1 0 $a Nonparametric Kernel Density Estimation and Its Computational Aspects $h [electronic resource] / $c by Artur Gramacki.
250 $a 1st ed. 2018.
264 1 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2018.
300 $a XXIX, 176 p. 70 illus. $b online resource.
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
347 $a text file $b PDF $2 rda
490 1 $a Studies in Big Data, $x 2197-6503 ; $v 37
520 $a This book describes computational problems related to kernel density estimation (KDE) – one of the most important and widely used data smoothing techniques. A very detailed description of novel FFT-based algorithms for both KDE computations and bandwidth selection are presented. The theory of KDE appears to have matured and is now well developed and understood. However, there is not much progress observed in terms of performance improvements. This book is an attempt to remedy this. The book primarily addresses researchers and advanced graduate or postgraduate students who are interested in KDE and its computational aspects. The book contains both some background and much more sophisticated material, hence also more experienced researchers in the KDE area may find it interesting. The presented material is richly illustrated with many numerical examples using both artificial and real datasets. Also, a number of practical applications related to KDE are presented.
650 0 $a Computational intelligence. $3 568984
650 0 $a Artificial intelligence. $3 559380
650 0 $a Big data. $3 981821
650 1 4 $a Computational Intelligence. $3 768837
650 2 4 $a Artificial Intelligence. $3 646849
650 2 4 $a Big Data. $3 1017136
710 2 $a SpringerLink (Online service) $3 593884
773 0 $t Springer Nature eBook
776 0 8 $i Printed edition: $z 9783319716879
776 0 8 $i Printed edition: $z 9783319716893
776 0 8 $i Printed edition: $z 9783319890944
830 0 $a Studies in Big Data, $x 2197-6503 ; $v 8 $3 1256918
856 4 0 $u https://doi.org/10.1007/978-3-319-71688-6
912 $a ZDB-2-ENG
912 $a ZDB-2-SXE
950 $a Engineering (SpringerNature-11647)
950 $a Engineering (R0) (SpringerNature-43712)