Optimal linear regression on classifier outputs

1. Bates, J.M. and Granger, C.W.J. (1969): The Combination of Forecasts. Operational Research Quaterly, Vol. 20, 451–468.

   Google Scholar 

2. Bishop, C.M. (1995): Neural Networks for Pattern Recognition, Clarendon Press, Oxford.

   Google Scholar 

3. Guermeur, Y., d'Alché-Buc, F. and Gallinari, P. (1997): Combinaison Linéaire Optimale de Classifieurs. XXIX-ièmes Journées de Statistique, 425–428.

   Google Scholar 

4. Guermeur, Y. and Gallinari, P. (1996): Combining Statistical Models for Protein Secondary Structure Prediction. ICANN'96, Bochum, 599–604.

   Google Scholar 

5. LeBlanc, M. and Tibshirani, R. (1993): Combining estimates in regression and classification. Technical Report 9318, Department of Preventive Medicine and Biostatistics and Department of Statistics, University of Toronto.

   Google Scholar 

6. Rosen, J.B. (1960): The Gradient Projection Method for Nonlinear Programming. Part 1. Linear Constraints. J. Soc. Indust. Appl. Math., Vol. 8, N ^o 1, 181–217.

   Google Scholar 

7. Perrone, M.P. and Cooper, L.N. (1993): When Networks Disagree: Ensemble Methods for Hybrid Neural Networks. Technical Report, Institute for Brain and Neural Systems, Brown University, Providence, Rhode Island.

   Google Scholar 

8. Rost, B. and Sander, C. (1993): Prediction of Protein Secondary Structure at Better than 70% Accuracy. J. Mol. Biol., 232, 584–599.

   Google Scholar 

9. Wolpert, D.H. (1992): Stacked Generalization. Neural Networks, Vol. 5, 241–259.

   Google Scholar 

Download references