In this paper, mixture of experts model is first applied to stellar data classification. In order to obtain input patterns of mixture of experts model, we present a feature extraction method for stellar data based on wavelet packet transformation. Then a mixture of experts model is built for classifying the feature vectors. A comparative study of different classification methods such as a single radial basis function neural network is given. Real world data experimental results show that the mixture of experts has a good generalization ability and the obtained correct classification rate is higher than that of using a single neural network. © Springer-Verlag Berlin Heidelberg 2005.
CITATION STYLE
Jiang, Y., & Guo, P. (2005). Mixture of experts for stellar data classification. In Lecture Notes in Computer Science (Vol. 3497, pp. 310–315). Springer Verlag. https://doi.org/10.1007/11427445_50
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