Ensembling neural networks: Many could be better than all

Bauer, 1999, An empirical comparison of voting classification algorithms: Bagging, Boosting, and variants, Machine Learning, 36, 105, 10.1023/A:1007515423169

Blake, 1998

Breiman, 1996, Bagging predictors, Machine Learning, 24, 123, 10.1007/BF00058655

Breiman, 1996

Cherkauer, 1996, Human expert level performance on a scientific image analysis task by a system using combined artificial neural networks, 15

Cunningham, 2000, Stability problems with artificial neural networks and the ensemble solution, Artificial Intelligence in Medicine, 20, 217, 10.1016/S0933-3657(00)00065-8

Demuth, 1998

Drucker, 1999, Boosting using neural nets, 51

Drucker, 1993, Improving performance in neural networks using a boosting algorithm, 42

Efron, 1993

Freund, 1995, Boosting a weak algorithm by majority, Inform. and Comput., 121, 256, 10.1006/inco.1995.1136

Freund, 1995, A decision-theoretic generalization of on-line learning and an application to boosting, 23

Freund, 1996, Experiments with a new boosting algorithm, 148

German, 1992, Neural networks and the bias/variance dilemma, Neural Comput., 4, 1, 10.1162/neco.1992.4.1.1

Goldberg, 1989

Gutta, 1996, Face recognition using hybrid classifier systems, 1017

Hampshire, 1990, A novel objective function for improved phoneme recognition using time-delay neural networks, IEEE Trans. Neural Networks, 1, 216, 10.1109/72.80233

Hansen, 2000

Hansen, 1992, Ensemble methods for handwritten digit recognition, 333

Hansen, 1990, Neural network ensembles, IEEE Trans. Pattern Anal. Machine Intelligence, 12, 993, 10.1109/34.58871

Houck, 1995

Huang, 2000, Pose invariant face recognition, 245

Jacobs, 1991, Adaptively mixtures of local experts, Neural Comput., 3, 79, 10.1162/neco.1991.3.1.79

Jimenez, 1998, Dynamically weighted ensemble neural networks for classification, 753

Jordan, 1994, Hierarchical mixtures of experts and the EM algorithm, Neural Comput., 6, 181, 10.1162/neco.1994.6.2.181

Kohavi, 1996, Bias plus variance decomposition for zero-one loss functions, 275

Kong, 1995, Error-correcting output coding corrects bias and variance, 313

Krogh, 1995, Neural network ensembles, cross validation, and active learning, 231

Maclin, 1995, Combining the predictions of multiple classifiers: Using competitive learning to initialize neural networks, 524

Mao, 1998, A case study on bagging, boosting and basic ensembles of neural networks for OCR, 1828

Merz, 1997, Combining neural network regression estimates with regularized linear weights, 564

Opitz, 1999, Popular ensemble methods: An empirical study, J. Artificial Intelligence Res., 11, 169, 10.1613/jair.614

Opitz, 1996, Actively searching for an effective neural network ensemble, Connection Science, 8, 337, 10.1080/095400996116802

Opitz, 1996, Generating accurate and diverse members of a neural network ensemble, 535

Perrone, 1993, When networks disagree: Ensemble method for neural networks, 126

Quinlan, 1996, Bagging, Boosting, and C4.5, 725

Ridgeway, 1999, Boosting methodology for regression problems, 152

Rumelhart, 1986, Learning internal representations by error propagation, 1, 318

Schapire, 1990, The strength of weak learnability, Machine Learning, 5, 197, 10.1007/BF00116037

1999

Shimshoni, 1998, Classification of seismic signals by integrating ensembles of neural networks, IEEE Trans. Signal Process., 46, 1194, 10.1109/78.668782

Sollich, 1996, Learning with ensembles: How over-fitting can be useful, 190

Ueda, 2000, Optimal linear combination of neural networks for improving classification performance, IEEE Trans. Pattern Anal. Machine Intelligence, 22, 207, 10.1109/34.825759

Weston, 1996

Wolpert, 1992, Stacked generalization, Neural Networks, 5, 241, 10.1016/S0893-6080(05)80023-1

Yao, 1998, Making use of population information in evolutionary artificial neural networks, IEEE Transactions on Systems, Man and Cybernetics—Part B: Cybernetics, 28, 417, 10.1109/3477.678637

Zhou, 2002, Lung cancer cell identification based on artificial neural network ensembles, Artificial Intelligence in Medicine, 24, 25, 10.1016/S0933-3657(01)00094-X