Data classification with radial basis function networks based on a novel kernel density estimation algorithm

@article{Oyang2005DataCW,
  title={Data classification with radial basis function networks based on a novel kernel density estimation algorithm},
  author={Yen-Jen Oyang and Shien-Ching Hwang and Yu-Yen Ou and Chien-Yu Chen and Zhi-Wei Chen},
  journal={IEEE Transactions on Neural Networks},
  year={2005},
  volume={16},
  pages={225-236}
}
This work presents a novel learning algorithm for efficient construction of the radial basis function (RBF) networks that can deliver the same level of accuracy as the support vector machines (SVMs) in data classification applications. The proposed learning algorithm works by constructing one RBF subnetwork to approximate the probability density function of each class of objects in the training data set. With respect to algorithm design, the main distinction of the proposed learning algorithm… CONTINUE READING
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