The Strength of Weak Learnability
Machine Learning
Original Contribution: Stacked generalization
Neural Networks
Boosting a weak learning algorithm by majority
Information and Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information, Prediction, and Query by Committee
Advances in Neural Information Processing Systems 5, [NIPS Conference]
Learning and Evolution by Minimization of Mutual Information
PPSN VII Proceedings of the 7th International Conference on Parallel Problem Solving from Nature
Applying Boosting Techniques to Genetic Programming
Selected Papers from the 5th European Conference on Artificial Evolution
Boosting Kernel Models for Regression
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
IEEE Transactions on Evolutionary Computation
Evolutionary ensembles with negative correlation learning
IEEE Transactions on Evolutionary Computation
GP ensembles for large-scale data classification
IEEE Transactions on Evolutionary Computation
Genetic Programming and Evolvable Machines
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We describe a data mining framework that derives panelist information from sparse flavour survey data. One component of the framework executes genetic programming ensemble based symbolic regression. Its evolved models for each panelist provide a second component with all plausible and uncorrelated explanations of how a panelist rates flavours. The second component bootstraps the data using an ensemble selected from the evolved models, forms a probability density function for each panelist and clusters the panelists into segments that are easy to please, neutral, and hard to please.