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machine learning
boosting
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Korrektur des Tätigkeitsschlüssels der Bundesagentur für Arbeit mithilfe maschineller Lernverfahren
Schmidt, Elena
- In:
WISTA – Wirtschaft und Statistik
72
(
2020
)
6
,
pp. 37-47
Ziel des vorgestellten Projekts war, das Merkmal Vollzeit/Teilzeit des Tätigkeitsschlüssels in den Integrierten Erwerbsbiografien der Bundesagentur für Arbeit zu korrigieren. Dies sollte mithilfe des auch in der Verdienststrukturerhebung vorhandenen, aber manuell korrigierten Schlüssels...
Persistent link: https://www.econbiz.de/10012392752
Saved in:
2
Development of machine learning-based real time scheduling systems : using
ensemble
based on wrapper feature selection approach
Shiue, Yeou-ren
;
Guh, Ruey-shiang
;
Lee, Ken-chun
- In:
International journal of production research
50
(
2012
)
20
,
pp. 5887-5905
Persistent link: https://www.econbiz.de/10009670323
Saved in:
3
Machine learning-based classifiers
ensemble
for credit risk assessment
Pandey, Trilok Nath
;
Jagadev, Alok Kumar
;
Choudhury, D.
; …
- In:
International journal of electronic finance : IJEF
7
(
2013
)
3/4
,
pp. 227-249
Persistent link: https://www.econbiz.de/10010400393
Saved in:
4
A hybrid
ensemble
machine learning model to predict success of Bollywood movies
Verma, Garima
;
Verma, Hemraj
;
Dixit, Sushil Kumar
- In:
World review of entrepreneurship, management and …
17
(
2021
)
2/3
,
pp. 343-357
Persistent link: https://www.econbiz.de/10012515249
Saved in:
5
Korrektur des Tätigkeitsschlüssels der Bundesagentur für Arbeit mithilfe maschineller Lernverfahren
Schmidt, Elena
- In:
Wirtschaft und Statistik : WISTA
(
2020
)
6
,
pp. 37-47
Ziel des vorgestellten Projekts war, das Merkmal Vollzeit/Teilzeit des Tätigkeitsschlüssels in den Integrierten Erwerbsbiografien der Bundesagentur für Arbeit zu korrigieren. Dies sollte mithilfe des auch in der Verdienststrukturerhebung vorhandenen, aber manuell korrigierten Schlüssels...
Persistent link: https://www.econbiz.de/10012388652
Saved in:
6
Data analytics for non-life insurance pricing
Wüthrich, Mario V.
;
Buser, Christoph
-
2017
-
Draft lecture notes, version January 27, 2017
regression trees, bagging, random forest,
boosting
machines and neural networks. Finally, we provide methodologies for analysing …
Persistent link: https://www.econbiz.de/10011625588
Saved in:
7
Macroeconomic forecasting using factor models and machine learning : an application to Japan
Maehashi, Kohei
;
Shintani, Mototsugu
-
2020
Persistent link: https://www.econbiz.de/10013329759
Saved in:
8
Bagging or
boosting
? : empirical evidence from financial statement fraud detection
Chen, Xiaowei
;
Zhai, Cong
- In:
Accounting and finance
63
(
2023
)
5
,
pp. 5093-5142
Persistent link: https://www.econbiz.de/10014477220
Saved in:
9
Machine learning advances for time series forecasting
Masini, Ricardo P.
;
Medeiros, Marcelo C.
;
Mendes, Eduardo F.
- In:
Journal of economic surveys
37
(
2023
)
1
,
pp. 76-111
Persistent link: https://www.econbiz.de/10014287800
Saved in:
10
Nowcasting GDP: what are the gains from machine learning algorithms?
Arro-Cannarsa, Milen
;
Scheufele, Rolf
-
2024
Persistent link: https://www.econbiz.de/10015044842
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