Active learning in neural networks
New learning paradigms in soft computing
Model Generation of Neural Network Ensembles Using Two-Level Cross-Validation
ICCS '01 Proceedings of the International Conference on Computational Science-Part II
Reducing the Training Times of Neural Classifiers with Dataset Condensing
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Experimental design of supervisory control functions based on multilayer perceptrons
Artificial Intelligence for Engineering Design, Analysis and Manufacturing
Biometrics and Identity Management
Fuzzy model validation using the local statistical approach
Fuzzy Sets and Systems
Active learning for probabilistic neural networks
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
Musical sound recognition by active learning PNN
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Hi-index | 0.00 |
We propose a new approach for leave-one-out cross-validation of neural-network classifiers called “cross-validation with active pattern selection” (CV/APS). In CV/APS, the contribution of the training patterns to network learning is estimated and this information is used for active selection of CV patterns. On the tested examples, the computational cost of CV can be drastically reduced with only small or no errors