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Many real-world classification problems are characterized by samples of a complex distribution in the input space. The classification accuracy is determined by intrinsic properties of all samples in subspaces of features. This paper proposes a novel algorithm for the construction of radial basis function neural network (RBFNN) classifier based on subspace learning. In this paper, feature subspaces...
The diversity of individual neural network will affect the forecast error of neural network ensemble. In this paper, a new training method for neural network ensemble which is based on the improved K-means algorithm is presented. The space diversity among the sample datasets can be realized by clustering the entire dataset using K-means algorithm. To avoiding the sample subset too simple, the sample...
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