A kernel logistic neural network based on restricted Boltzmann machine
Lv Q.1; Li H.1; Chen C.L.P.3; Wang D.1; Song W.4; Lin H.2
Source Publication2016 3rd International Conference on Informative and Cybernetics for Computational Social Systems, ICCSS 2016
AbstractA multi-class classification technique which combines kernel logistic neural network (KLNN) and restricted Boltzmann machine (RBM), called KLNN-RBM, is designed. The principal component analysis (PCA) is applied to determine the dimension of the kernel function. The initial weights and thresholds of this model are obtained by RBM. Then, the maximum likelihood estimate with a ridge regularization term and a new stochastic gradient descent method with a scaling factor are used to optimize the parameters in order to realize the multi-class classification. Some numerical simulations illustrate the validity of the proposed method.
Keywordkernel logistic neural network maximum likelihood estimate principal component analysis restricted Boltzmann machine ridge regularization
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Document TypeConference paper
CollectionUniversity of Macau
Affiliation1.Dalian University of Technology
2.Dalian Medical University
3.Universidade de Macau
4.Dongbei University of Finance and EcoNomics
Recommended Citation
GB/T 7714
Lv Q.,Li H.,Chen C.L.P.,et al. A kernel logistic neural network based on restricted Boltzmann machine[C],2016:1-6.
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