Machine Learning
Machine Learning
Locally Weighted Learning for Control
Artificial Intelligence Review - Special issue on lazy learning
Lazy learning
Boosting regression estimators
Neural Computation
Data mining: practical machine learning tools and techniques with Java implementations
Data mining: practical machine learning tools and techniques with Java implementations
Reduction Techniques for Instance-BasedLearning Algorithms
Machine Learning
Using Iterated Bagging to Debias Regressions
Machine Learning
Advances in Instance Selection for Instance-Based Learning Algorithms
Data Mining and Knowledge Discovery
A Principal Components Approach to Combining Regression Estimates
Machine Learning
Boosting Methods for Regression
Machine Learning
ECML '95 Proceedings of the 8th European Conference on Machine Learning
Inference for the Generalization Error
Machine Learning
Hierarchical Gaussian process mixtures for regression
Statistics and Computing
Managing Diversity in Regression Ensembles
The Journal of Machine Learning Research
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
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Many learning problems involve an exploration of relationships between features in heterogeneous datasets, where different learning models can be more suitable for different regions. We propose herein a technique of localized averaging of regression models. This technique identifies local regions which have similar characteristics and then uses the average value of local experts to describe the relationship between the predictive feature values and the target value. We performed a comparison with other famous combining methods on standard benchmark datasets, and the correlation coefficient of the proposed method was higher.