Search Results - subject:"overfitting"

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Estimating production functions through additive models based on regression splines

España, Victor J.; Aparicio, Juan; Barber, Xavier; … - In: European journal of operational research : EJOR 312 (2024) 2, pp. 684-699

Persistent link: https://www.econbiz.de/10014456317

Convex support vector regression

Liao, Zhiqiang; Dai, Sheng; Kuosmanen, Timo - In: European journal of operational research : EJOR 313 (2024) 3, pp. 858-870

Persistent link: https://www.econbiz.de/10014456645

The virtue of transparency : how to maximize the utility of data without overfitting

Czasonis, Megan; Kritzman, Mark; Turkington, David - 2024 - This version: July 22, 2024

Persistent link: https://www.econbiz.de/10014583528

Measuring technical efficiency for multi-input multi-output production processes through OneClass Support Vector Machines : a finite-sample study

Moragues, Raul; Aparicio, Juan; Esteve, Miriam - In: Operational research : an international journal 23 (2023) 3, pp. 1-33

Persistent link: https://www.econbiz.de/10014364592

Machine learning in finance : major applications, issues, metrics, and future trends

Al-Maskati, Nawaf - In: International journal of financial engineering 9 (2022) 3, pp. 2250010-1-2250010-32

Persistent link: https://www.econbiz.de/10013367620

QuantNet : transferring learning across trading strategies

Koshiyama, Adriano; Blumberg, Stefano B.; Firoozye, Nikan B. - In: Quantitative finance 22 (2022) 6, pp. 1071-1090

Persistent link: https://www.econbiz.de/10013367886

A bayesian approach to measurement of backtest overfitting

Witzany, Jiří - In: Risks 9 (2021) 1, pp. 1-22

historical data. Standard statistical techniques to prevent model overfitting such as out-sample backtesting turn out to be … ongoing discussion of how to estimate the probability of backtest overfitting and adjust the expected performance indicators …

Persistent link: https://www.econbiz.de/10013200688

A bayesian approach to measurement of backtest overfitting

Witzany, Jiří - In: Risks : open access journal 9 (2021) 1/18, pp. 1-22

Persistent link: https://www.econbiz.de/10012423034

Machine Learning Anwendungen in der betrieblichen Praxis: Praktische Empfehlungen zur betrieblichen Mitbestimmung

Thieltges, Andree - 2020

KI-Modelle und Machine-Learning-Anwendungen halten Einzug in die alltägliche Praxis von Unternehmen und können mitbestimmt werden. Um die Interessen und Rechte der Beschäftigten zu berücksichtigen und zu schützen, sollten die aktuellen Regelungen in betrieblichen IT-Vereinbarungen...

Persistent link: https://www.econbiz.de/10012669333

Investor sentiment as a predictor of market returns

Kaivanto, Kim; Zhang, Peng - 2019

Persistent link: https://www.econbiz.de/10012193287

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