語系:
繁體中文
English
說明(常見問題)
登入
回首頁
切換:
標籤
|
MARC模式
|
ISBD
Improved Classification Rates for Lo...
~
Blaschzyk, Ingrid Karin.
Improved Classification Rates for Localized Algorithms under Margin Conditions
紀錄類型:
書目-語言資料,印刷品 : Monograph/item
正題名/作者:
Improved Classification Rates for Localized Algorithms under Margin Conditions/ by Ingrid Karin Blaschzyk.
作者:
Blaschzyk, Ingrid Karin.
面頁冊數:
XV, 126 p. 5 illus. in color.online resource. :
Contained By:
Springer Nature eBook
標題:
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. -
電子資源:
https://doi.org/10.1007/978-3-658-29591-2
ISBN:
9783658295912
Improved Classification Rates for Localized Algorithms under Margin Conditions
Blaschzyk, Ingrid Karin.
Improved Classification Rates for Localized Algorithms under Margin Conditions
[electronic resource] /by Ingrid Karin Blaschzyk. - 1st ed. 2020. - XV, 126 p. 5 illus. in color.online resource.
Introduction to Statistical Learning Theory -- Histogram Rule: Oracle Inequality and Learning Rates -- Localized SVMs: Oracle Inequalities and Learning Rates.
Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chunking method for large-scale learning problems, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guarantees, which in particular include classification rates. The statistical analysis relies on a new and simple partitioning based technique and takes well-known margin conditions into account that describe the behavior of the data-generating distribution. It turns out that the rates outperform known rates of several other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and validation procedure achieves the theoretical rates adaptively, that is, without knowing the margin parameters in advance. Contents Introduction to Statistical Learning Theory Histogram Rule: Oracle Inequality and Learning Rates Localized SVMs: Oracle Inequalities and Learning Rates Target Groups Researchers, students, and practitioners in the fields of mathematics and computer sciences who focus on machine learning or statistical learning theory The Author Ingrid Karin Blaschzyk is a postdoctoral researcher in the Department of Mathematics at the University of Stuttgart, Germany.
ISBN: 9783658295912
Standard No.: 10.1007/978-3-658-29591-2doiSubjects--Topical Terms:
782247
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
LC Class. No.: T57-57.97
Dewey Class. No.: 519
Improved Classification Rates for Localized Algorithms under Margin Conditions
LDR
:02950nam a22003855i 4500
001
1022897
003
DE-He213
005
20200706032752.0
007
cr nn 008mamaa
008
210318s2020 gw | s |||| 0|eng d
020
$a
9783658295912
$9
978-3-658-29591-2
024
7
$a
10.1007/978-3-658-29591-2
$2
doi
035
$a
978-3-658-29591-2
050
4
$a
T57-57.97
072
7
$a
PBW
$2
bicssc
072
7
$a
MAT003000
$2
bisacsh
072
7
$a
PBW
$2
thema
082
0 4
$a
519
$2
23
100
1
$a
Blaschzyk, Ingrid Karin.
$e
author.
$4
aut
$4
http://id.loc.gov/vocabulary/relators/aut
$3
1318693
245
1 0
$a
Improved Classification Rates for Localized Algorithms under Margin Conditions
$h
[electronic resource] /
$c
by Ingrid Karin Blaschzyk.
250
$a
1st ed. 2020.
264
1
$a
Wiesbaden :
$b
Springer Fachmedien Wiesbaden :
$b
Imprint: Springer Spektrum,
$c
2020.
300
$a
XV, 126 p. 5 illus. in color.
$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
505
0
$a
Introduction to Statistical Learning Theory -- Histogram Rule: Oracle Inequality and Learning Rates -- Localized SVMs: Oracle Inequalities and Learning Rates.
520
$a
Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chunking method for large-scale learning problems, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guarantees, which in particular include classification rates. The statistical analysis relies on a new and simple partitioning based technique and takes well-known margin conditions into account that describe the behavior of the data-generating distribution. It turns out that the rates outperform known rates of several other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and validation procedure achieves the theoretical rates adaptively, that is, without knowing the margin parameters in advance. Contents Introduction to Statistical Learning Theory Histogram Rule: Oracle Inequality and Learning Rates Localized SVMs: Oracle Inequalities and Learning Rates Target Groups Researchers, students, and practitioners in the fields of mathematics and computer sciences who focus on machine learning or statistical learning theory The Author Ingrid Karin Blaschzyk is a postdoctoral researcher in the Department of Mathematics at the University of Stuttgart, Germany.
650
2 4
$a
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
$3
782247
650
2 4
$a
Probability Theory and Stochastic Processes.
$3
593945
650
1 4
$a
Applications of Mathematics.
$3
669175
650
0
$a
Statistics .
$3
1253516
650
0
$a
Probabilities.
$3
527847
650
0
$a
Engineering mathematics.
$3
562757
650
0
$a
Applied mathematics.
$3
1069907
710
2
$a
SpringerLink (Online service)
$3
593884
773
0
$t
Springer Nature eBook
776
0 8
$i
Printed edition:
$z
9783658295905
776
0 8
$i
Printed edition:
$z
9783658295929
856
4 0
$u
https://doi.org/10.1007/978-3-658-29591-2
912
$a
ZDB-2-SMA
912
$a
ZDB-2-SXMS
950
$a
Mathematics and Statistics (SpringerNature-11649)
950
$a
Mathematics and Statistics (R0) (SpringerNature-43713)
筆 0 讀者評論
多媒體
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼[密碼必須為2種組合(英文和數字)及長度為10碼以上]
登入