語系:
繁體中文
English
說明(常見問題)
圖資館首頁
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-電子資源 : Monograph/item
正題名/作者:
Improved classification rates for localized algorithms under margin conditionsby Ingrid Karin Blaschzyk.
作者:
Blaschzyk, Ingrid Karin.
出版者:
Wiesbaden :Springer Fachmedien Wiesbaden :2020.
面頁冊數:
xv, 126 p. :ill., digital ;24 cm.
Contained By:
Springer eBooks
標題:
Discriminant analysis.
電子資源:
https://doi.org/10.1007/978-3-658-29591-2
ISBN:
9783658295912$q(electronic bk.)
Improved classification rates for localized algorithms under margin conditions
Blaschzyk, Ingrid Karin.
Improved classification rates for localized algorithms under margin conditions
[electronic resource] /by Ingrid Karin Blaschzyk. - Wiesbaden :Springer Fachmedien Wiesbaden :2020. - xv, 126 p. :ill., digital ;24 cm.
Introduction to Statistical Learning Theory -- Histogram Rule: Oracle Inequality and Learning Rates -- Localized SVMs: Oracle Inequalities and Learning Rates.
Support vector machines (SVMs) are one of the most successful algorithms on small and medium-sized data sets, but on large-scale data sets their training and predictions become computationally infeasible. The author considers a spatially defined data chunking method for large-scale learning problems, leading to so-called localized SVMs, and implements an in-depth mathematical analysis with theoretical guarantees, which in particular include classification rates. The statistical analysis relies on a new and simple partitioning based technique and takes well-known margin conditions into account that describe the behavior of the data-generating distribution. It turns out that the rates outperform known rates of several other learning algorithms under suitable sets of assumptions. From a practical point of view, the author shows that a common training and validation procedure achieves the theoretical rates adaptively, that is, without knowing the margin parameters in advance. Contents Introduction to Statistical Learning Theory Histogram Rule: Oracle Inequality and Learning Rates Localized SVMs: Oracle Inequalities and Learning Rates Target Groups Researchers, students, and practitioners in the fields of mathematics and computer sciences who focus on machine learning or statistical learning theory The Author Ingrid Karin Blaschzyk is a postdoctoral researcher in the Department of Mathematics at the University of Stuttgart, Germany.
ISBN: 9783658295912$q(electronic bk.)
Standard No.: 10.1007/978-3-658-29591-2doiSubjects--Topical Terms:
182521
Discriminant analysis.
LC Class. No.: QA278.65 / .B537 2020
Dewey Class. No.: 519.535
Improved classification rates for localized algorithms under margin conditions
LDR
:02620nmm a2200325 a 4500
001
572810
003
DE-He213
005
20200806112040.0
006
m d
007
cr nn 008maaau
008
200925s2020 gw s 0 eng d
020
$a
9783658295912$q(electronic bk.)
020
$a
9783658295905$q(paper)
024
7
$a
10.1007/978-3-658-29591-2
$2
doi
035
$a
978-3-658-29591-2
040
$a
GP
$c
GP
041
0
$a
eng
050
4
$a
QA278.65
$b
.B537 2020
072
7
$a
PBW
$2
bicssc
072
7
$a
MAT003000
$2
bisacsh
072
7
$a
PBW
$2
thema
082
0 4
$a
519.535
$2
23
090
$a
QA278.65
$b
.B644 2020
100
1
$a
Blaschzyk, Ingrid Karin.
$3
860024
245
1 0
$a
Improved classification rates for localized algorithms under margin conditions
$h
[electronic resource] /
$c
by Ingrid Karin Blaschzyk.
260
$a
Wiesbaden :
$b
Springer Fachmedien Wiesbaden :
$b
Imprint: Springer Spektrum,
$c
2020.
300
$a
xv, 126 p. :
$b
ill., digital ;
$c
24 cm.
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
0
$a
Discriminant analysis.
$3
182521
650
0
$a
Support vector machines.
$3
679056
650
1 4
$a
Applications of Mathematics.
$3
273744
650
2 4
$a
Probability Theory and Stochastic Processes.
$3
274061
650
2 4
$a
Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.
$3
348605
710
2
$a
SpringerLink (Online service)
$3
273601
773
0
$t
Springer eBooks
856
4 0
$u
https://doi.org/10.1007/978-3-658-29591-2
950
$a
Mathematics and Statistics (Springer-11649)
筆 0 讀者評論
全部
電子館藏
館藏
1 筆 • 頁數 1 •
1
條碼號
館藏地
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
000000179421
電子館藏
1圖書
電子書
EB QA278.65 .B644 2020 2020
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
多媒體檔案
https://doi.org/10.1007/978-3-658-29591-2
評論
新增評論
分享你的心得
Export
取書館別
處理中
...
變更密碼
登入