Forecasting the insolvency of US banks using Support Vector Machines (SVMs) based on local learning feature selection
Year of publication: |
2013
|
---|---|
Authors: | Papadimitriou, Theophilos ; Gkonkas, Periklēs ; Plakandaras, Vasilios ; Mourmouris, John C. |
Published in: |
International journal of computational economics and econometrics. - Genève [u.a.] : Inderscience Enterprises, ISSN 1757-1170, ZDB-ID 2550146-X. - Vol. 3.2013, 1/2, p. 83-90
|
Subject: | bank insolvency | SVM | support vector machine | local learning | feature selection | Prognoseverfahren | Forecasting model | Mustererkennung | Pattern recognition | Insolvenz | Insolvency | Bankinsolvenz | Bank failure | Lernen | Learning | Lernprozess | Learning process | Theorie | Theory |
-
Shrivastava, Santosh Kumar, (2020)
-
Liu, Hongcheng, (2022)
-
Predicting bank insolvencies using machine learning techniques
Petropoulos, Anastasios, (2020)
- More ...
-
Papadimitriou, Theophilos, (2013)
-
Forecasting credit ratings of EU banks
Plakandaras, Vasilios, (2020)
-
Macroeconomic uncertainty, growth and inflation in the Eurozone : a causal approach
Plakandaras, Vasilios, (2018)
- More ...