RSS for Using Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networkshttp://academic.research.microsoft.com/Rss.aspx?cata=9&id=2202347Search RSS feed for Microsoft Academic SearchMSRA Libra RSS Burner(c)2008 Microsoft Corpration, All right reserved.Tue, 21 May 2013 13:36:50 GMTTue, 21 May 2013 13:36:50 GMTUsing Regression Error Characteristic Curves for Model Selection in Ensembles of Neural Networkshttp://academic.research.microsoft.com/Publication/2202347Tue, 21 May 2013 06:36:50 GMT22023470Aloísio Carlos de Pina, Gerson Zaverucha: view publication
Regression Error Characteristic (REC) analysis is a technique for evaluation and comparison of regression models that facilitates the visualization of the performance of many regression functions simultaneously in a single graph. The objective of this work is to present a new approach for model selection in ensembles of Neural Networks, in which we propose the use of REC curves in order to select a good threshold value, so that only residuals greater than that value are considered as errors. The algorithm was empirically evaluated and its results were analyzed also by means of REC curves.
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