Abstract
This paper presents a new compensation mechanism to be used with a Partial Logistic Artificial Neural Network for Competing Risks with Automatic Relevance Determination (PLANN-CR-ARD) and tested comprehensibly on a real breast cancer dataset with excellent convergence properties and numerical stability for the non-linear model. The Model Selection is implemented for the PLANN-CR-ARD model, benefiting from a scaling of the prior error term which together with the data error term forms the total error function that is optimized. The PLANN-CR-ARD proves to be an excellent prognostic tool that can be used in regression analysis tasks such as the survival analysis of cancer datasets. © 2011 Springer-Verlag.
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CITATION STYLE
Arsene, C. T. C., Lisboa, P. J., & Biganzoli, E. (2011). Model selection with PLANN-CR-ARD. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 6692 LNCS, pp. 210–219). https://doi.org/10.1007/978-3-642-21498-1_27
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