Book ChapterDOI
Learning Classification RBF Networks by Boosting
Juan José Rodríguez Diez,Carlos Alonso González +1 more
- pp 43-52
TLDR
This work proposes a novel method for constructing RBF networks, based on boosting, where the task assigned to the base learner is to select a RBF, while the boosting algorithm combines linearly the different RBFs.Abstract:
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.read more
Citations
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Theoretical Interpretations and Applications of Radial Basis Function Networks
TL;DR: RBFNs' interpretations can suggest applications that are particularly interesting in medical domains, and a survey of their interpretations and of their correspond- ing learning algorithms is provided.
Proceedings ArticleDOI
P300 Detection Using Boosting Neural Networks with Application to BCI
Jiacai Zhang,Yaqin Xu,Li Yao +2 more
TL;DR: A powerful methods is introduced to automatically detect P300 subcomponents in multi-channel electroencephalogram (EEG) trials and it is shown that the temporal features of P300 is more stable and discriminable than spatial features.
DissertationDOI
Temporal sensorfusion for the classification of bioacoustic time series
TL;DR: This thesis deals with classifier ensemble methods for time series classification applied to bioacoustic data and applies a wrapper feature selection method, the sequential forward selection, in order to determine further discriminative feature sets for the individual classifiers.
Book ChapterDOI
Building RBF Networks for Time Series Classification by Boosting
TL;DR: This work presents a learning system for the classification of multivariate time series that is useful in domains such as biomedical signals, continuous systems diagnosis, or data mining in temporal databases.
Proceedings ArticleDOI
Identification of vegetable diseases using neural network
Jiacai Zhang,Jianjun Tang,Yao Li +2 more
TL;DR: This paper explores the feasibility of implementing fast and reliable automatic identification of vegetable diseases and their infection grades from color and morphological features of leaves from the database collected by Chinese Academy of Agricultural Sciences.
References
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Journal ArticleDOI
Classification and Regression Trees.
Journal ArticleDOI
A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting
Yoav Freund,Robert E. Schapire +1 more
TL;DR: The model studied can be interpreted as a broad, abstract extension of the well-studied on-line prediction model to a general decision-theoretic setting, and it is shown that the multiplicative weight-update Littlestone?Warmuth rule can be adapted to this model, yielding bounds that are slightly weaker in some cases, but applicable to a considerably more general class of learning problems.
Proceedings Article
Experiments with a new boosting algorithm
Yoav Freund,Robert E. Schapire +1 more
TL;DR: This paper describes experiments carried out to assess how well AdaBoost with and without pseudo-loss, performs on real learning problems and compared boosting to Breiman's "bagging" method when used to aggregate various classifiers.
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