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Optimal Use of a Trained Neural Network for Input Selection.

, and . IWANN (2), volume 1607 of Lecture Notes in Computer Science, page 506-515. Springer, (1999)

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Optimal Use of a Trained Neural Network for Input Selection., and . IWANN (2), volume 1607 of Lecture Notes in Computer Science, page 506-515. Springer, (1999)New Results on Combination Methods for Boosting Ensembles., , and . ICANN (1), volume 5163 of Lecture Notes in Computer Science, page 285-294. Springer, (2008)Using Bagging and Cross-Validation to Improve Ensembles Based on Penalty Terms., , and . ICONIP (2), volume 7063 of Lecture Notes in Computer Science, page 588-595. Springer, (2011)Improving Boosting Methods by Generating Specific Training and Validation Sets., , and . ICONIP (2), volume 7063 of Lecture Notes in Computer Science, page 580-587. Springer, (2011)A new rule extraction algorithm based on interval arithmetic., , and . ESANN, page 155-160. (2003)Weight initialization methods for multilayer feedforward., and . ESANN, page 119-124. (2001)Training Radial Basis Functions by Gradient Descent., , , and . ICAISC, volume 3070 of Lecture Notes in Computer Science, page 184-189. Springer, (2004)Designing a Multilayer Feedforward Ensembles with Cross Validated Boosting Algorithm., , and . IJCNN, page 1278-1283. IEEE, (2006)Generalization capability of one and two hidden layers., and . IJCNN, page 1840-1843. IEEE, (1999)Multilayer Feedforward Ensembles for Classification Problems., , and . ICONIP, volume 3316 of Lecture Notes in Computer Science, page 744-749. Springer, (2004)