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  • Search: subject:"gradientboosting"
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Year of publication
Subject
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Artificial intelligence 5 Künstliche Intelligenz 5 Forecasting model 3 Neural networks 3 Neuronale Netze 3 Prognoseverfahren 3 Theorie 3 Theory 3 Arbitrage 2 Credit rating 2 Credit risk 2 Forestry 2 Forstwirtschaft 2 Insolvency 2 Insolvenz 2 Kreditrisiko 2 Kreditwürdigkeit 2 Learning process 2 Lernprozess 2 deep learning 2 ensemble learning 2 gradient-boosting 2 machine learning 2 random forest 2 random forests 2 Aktienmarkt 1 Classification 1 Credit derivative 1 Decision tree 1 Deep learning 1 Ensemble learning 1 Entscheidungsbaum 1 Estimation 1 Finance 1 Forecast 1 Forest policy 1 Forstpolitik 1 Gradient-boosting 1 Hypothek 1 Klassifikation 1
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Online availability
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Free 4 Undetermined 2 CC license 1
Type of publication
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Article 3 Book / Working Paper 3
Type of publication (narrower categories)
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Article in journal 3 Aufsatz in Zeitschrift 3 Working Paper 3 Arbeitspapier 2 Graue Literatur 2 Non-commercial literature 2
Language
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English 6
Author
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Do, Xuan Anh 3 Huck, Nicolas 3 Krauss, Christopher 3 Chi, Guotai 1 Elizarov, Pavel 1 Habib, Tabassum 1 Krasovytskyi, Danylo 1 Leonova, Aleksandra 1 Pyrlik, Vladimir 1 Stavytskyy, Andriy 1 Uddin, Mohammad S. 1 Zhou, Ying 1
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Published in...
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European journal of operational research : EJOR 1 FAU Discussion Papers in Economics 1 FAU discussion papers in economics 1 Mokslo darbai / Vilniaus Universitetas 1 The journal of risk model validation 1 Working paper series / CERGE-EI 1
Source
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ECONIS (ZBW) 5 EconStor 1
Showing 1 - 6 of 6
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Predicting mortgage loan defaults using machine learning techniques
Krasovytskyi, Danylo; Stavytskyy, Andriy - In: Mokslo darbai / Vilniaus Universitetas 103 (2024) 2, pp. 140-160
oversampling technique and compared the results. It was found that random forest and extreme gradient-boosting decision trees are …
Persistent link: https://www.econbiz.de/10015047688
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Forecasting realized volatility using machine learning and mixed-frequency data (the case of the Russian stock market)
Pyrlik, Vladimir; Elizarov, Pavel; Leonova, Aleksandra - 2021
Persistent link: https://www.econbiz.de/10013163805
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Deep neural networks, gradient-boosted trees, random forests: Statistical arbitrage on the S&P 500
Krauss, Christopher; Do, Xuan Anh; Huck, Nicolas - 2016
In recent years, machine learning research has gained momentum: New developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede well-known and powerful tree-based techniques mainly operating on the original feature space. All these methods...
Persistent link: https://www.econbiz.de/10011447705
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Cover Image
Deep neural networks, gradient-boosted trees, random forests : statistical arbitrage on the S&P 500
Krauss, Christopher; Do, Xuan Anh; Huck, Nicolas - 2016
In recent years, machine learning research has gained momentum: New developments in the field of deep learning allow for multiple levels of abstraction and are starting to supersede well-known and powerful tree-based techniques mainly operating on the original feature space. All these methods...
Persistent link: https://www.econbiz.de/10011447127
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Cover Image
An alternative statistical framework for credit default prediction
Uddin, Mohammad S.; Chi, Guotai; Habib, Tabassum; Zhou, Ying - In: The journal of risk model validation 14 (2020) 2, pp. 65-101
Persistent link: https://www.econbiz.de/10014335963
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Deep neural networks, gradient-boosted trees, random forests : statistical arbitrage on the S&P 500
Krauss, Christopher; Do, Xuan Anh; Huck, Nicolas - In: European journal of operational research : EJOR 259 (2017) 2, pp. 689-702
Persistent link: https://www.econbiz.de/10011661795
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