Title :
Support vector machines for company failure prediction
Author :
Yang, Zheng Rong
Author_Institution :
Dept. of Comput. Sci. & Eng., Exeter Univ., UK
Abstract :
This paper applies support vector machines (SM), a new powerful learning algorithm, to company failure prediction based on 2048 UK construction companies. The study shows that the SVM model outperforms linear statistical models and other neural network models.
Keywords :
construction industry; financial data processing; learning (artificial intelligence); learning automata; neural nets; statistical analysis; SVM; company failure prediction; construction companies; learning algorithm; linear statistical models; neural network models; support vector machines; Artificial neural networks; Computer science; Costs; Ear; Failure analysis; Machine learning; Neural networks; Power engineering and energy; Predictive models; Support vector machines;
Conference_Titel :
Computational Intelligence for Financial Engineering, 2003. Proceedings. 2003 IEEE International Conference on
Print_ISBN :
0-7803-7654-4
DOI :
10.1109/CIFER.2003.1196241
