The Strength of Weak Learnability
Machine Learning
Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
COLT '92 Proceedings of the fifth annual workshop on Computational learning theory
Neural networks and the bias/variance dilemma
Neural Computation
Numerical recipes in C (2nd ed.): the art of scientific computing
Numerical recipes in C (2nd ed.): the art of scientific computing
Original Contribution: Stacked generalization
Neural Networks
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Machine Learning
Exponentiated gradient versus gradient descent for linear predictors
Information and Computation
Information, Prediction, and Query by Committee
Advances in Neural Information Processing Systems 5, [NIPS Conference]
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
Classification and regression by combining models
Classification and regression by combining models
Improving model accuracy using optimal linear combinations of trained neural networks
IEEE Transactions on Neural Networks
On Fusers that Perform Better than Best Sensor
IEEE Transactions on Pattern Analysis and Machine Intelligence
Autonomous Agents and Multi-Agent Systems
IEEE Transactions on Knowledge and Data Engineering
Collaborative Filtering Using a Regression-Based Approach
Knowledge and Information Systems
IEEE Transactions on Knowledge and Data Engineering
Local averaging of heterogeneous regression models
International Journal of Hybrid Intelligent Systems
Non-strict heterogeneous Stacking
Pattern Recognition Letters
Pruning extensions to stacking
Intelligent Data Analysis
A fuzzy neural network with fuzzy impact grades
Neurocomputing
CIXL2: a crossover operator for evolutionary algorithms based on population features
Journal of Artificial Intelligence Research
Combining fingerprint and hand-geometry verification decisions
AVBPA'03 Proceedings of the 4th international conference on Audio- and video-based biometric person authentication
HiPC'05 Proceedings of the 12th international conference on High Performance Computing
Eigenclassifiers for combining correlated classifiers
Information Sciences: an International Journal
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
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The goal of combining the predictions of multiple learned models is to form an improved estimator. A combining strategy must be able to robustly handle the inherent correlation, or multicollinearity, of the learned models while identifying the unique contributions of each. A progression of existing approaches and their limitations with respect to these two issues are discussed. A new approach, PCR*, based on principal components regression is proposed to address these limitations. An evaluation of the new approach on a collection of domains reveals that (1) PCR* was the most robust combining method, (2) correlation could be handled without eliminating any of the learned models, and (3) the principal components of the learned models provided a continuum of “regularized” weights from which PCR* could choose.