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This paper investigates the construction of sparse radial basis function neural networks (RBFNNs) for classification problems. An efficient two-phase construction algorithm (which is abbreviated as TPCLR 1 for simplicity) is proposed by using L 1 regularization. In the first phase, an improved maximum data coverage (IMDC) algorithm is presented for the initialization of RBF centers and...
It is well known that dense coding with local bases (via Least Square coding schemes) can lead to large quantization errors or poor performances of machine learning tasks. On the other hand, sparse coding focuses on accurate representation without taking into account data locality due to its tendency to ignore the intrinsic structure hidden among the data. Local Hybrid Coding (LHC) (Xiang et al.,...
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