Finding patterns in time series: a dynamic programming approach
Advances in knowledge discovery and data mining
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Proceedings of the First International Workshop on Multiple Classifier Systems
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Using output codes to boost multiclass learning problems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Learning Comprehensible Descriptions of Multivariate Time Series
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Combining Multiple Models with Meta Decision Trees
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Learning First Order Logic Time Series Classifiers: Rules and Boosting
PKDD '00 Proceedings of the 4th European Conference on Principles of Data Mining and Knowledge Discovery
Applying Boosting to Similarity Literals for Time Series Classification
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
A Hybrid Projection Based and Radial Basis Function Architecture
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
COLT '00 Proceedings of the Thirteenth Annual Conference on Computational Learning Theory
Local feature extraction and its applications using a library of bases
Local feature extraction and its applications using a library of bases
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Decision trees can initialize radial-basis function networks
IEEE Transactions on Neural Networks
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This work proposes a novel method for constructing RBF networks, based on boosting. The task assigned to the base learner is to select a RBF, while the boosting algorithm combines linearly the different RBFs. For each iteration of boosting a new neuron is incorporated into the network. The method for selecting each RBF is based on randomly selecting several examples as the centers, considering the distances to these center as attributes of the examples and selecting the best split on one of these attributes. This selection of the best split is done in the same way than in the construction of decision trees. The RBF is computed from the center (attribute) and threshold selected. This work is not about using RBFNs as base learners for boosting, but about constructing RBFNs by boosting.