Mutual Information and Gamma Test for Input Selection
Abstract
In This Paper, Input Selection is Performed using Two Different Approaches. the First Approach is based on the Gamma Test. This Test Estimates the Mean Square Error (Mse) that Can Be Achieved Without overfitting. the Best Set of Inputs is the One that Minimises the Result of the Gamma Test. the Second Method Estimates the Mutual Information between a Set of Inputs and the Output. the Best Set of Inputs is the One that Maximises the Mutual Information. Both Methods Are Applied for the Selection of the Inputs for Function Approximation and Time Series Prediction Problems.
Recommended Citation
N. Reyhani et al., "Mutual Information and Gamma Test for Input Selection," ESANN 2005 Proceedings - 13th European Symposium on Artificial Neural Networks, pp. 503 - 508, European Symposium on Artificial Neural Networks, Dec 2007.
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-293030705-3
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 European Symposium on Artificial Neural Networks, All rights reserved.
Publication Date
01 Dec 2007
