Learning Classification RBF Networks by Boosting

Autor:	Carlos Alonso González, Juan José Rodríguez Diez
Rok vydání:	2001
Předmět:	ComputingMethodologies_PATTERNRECOGNITION Boosting (machine learning) Artificial neural network Computer science business.industry Decision tree Radial basis function Gradient boosting Artificial intelligence Machine learning computer.software_genre business computer
Zdroj:	Multiple Classifier Systems ISBN: 9783540422846 Multiple Classifier Systems
DOI:	10.1007/3-540-48219-9_5
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::6ae2bbf523ea0a663fb31eca65dfa93a https://doi.org/10.1007/3-540-48219-9_5 Zobrazit plný text záznamu