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- W2538139268 abstract "The kernel neuron is the generalization of classical McCulloch-Pitts neuron using Mercer kernels. In order to control generalization ability and prune structure of kernel neuron, we construct a regularized risk functional including both empirical risk functional and Laplace regularization term in this paper. Based on the gradient descent method, a novel training algorithm is designed, which is referred to as the sparse training procedure for kernel neuron. Such a procedure can realize three main ideas: kernel, regularization (or large margin) and sparseness in the kernel machines (e.g. support vector machines, kernel Fisher discriminant analysis, etc.), and can deal with the nonlinear classification and regression problems effectively." @default.
- W2538139268 created "2016-10-28" @default.
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- W2538139268 date "2003-01-01" @default.
- W2538139268 modified "2023-09-26" @default.
- W2538139268 title "Sparse training procedure for kernel neuron" @default.
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- W2538139268 doi "https://doi.org/10.1109/icnnsp.2003.1279210" @default.
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