A Kernel Logistic Neural Network based on Restricted Boltzmann Machine

被引：0

作者：

Lv, Qiuxia ^{[1
]}

Wang, Degang ^{[1
]}

Li, Hongxing ^{[1
]}

Song, Wenyan ^{[2
]}

Chen, C. L. Philip ^{[3
]}

Lin, Hongli ^{[4
]}

机构：

[1] Dalian Univ Technol, Sch Control Sci & Engn, Dalian 116024, Peoples R China

[2] Dongbei Univ Finance & Econ, Sch Math, Dalian 116025, Peoples R China

[3] Univ Macau, Fac Sci & Technol, Taipa, Macao, Peoples R China

[4] Dalian Med Univ, Dept Arephrol, Affiliated Hosp 1, 222 Zhongshan Rd, Dalian, Peoples R China

来源：

IEEE ICCSS 2016 - 2016 3RD INTERNATIONAL CONFERENCE ON INFORMATIVE AND CYBERNETICS FOR COMPUTATIONAL SOCIAL SYSTEMS (ICCSS) | 2016年

关键词：

kernel logistic neural network; restricted Boltzmann machine; principal component analysis; ridge regularization; maximum likelihood estimate; REGRESSION; CLASSIFICATION;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A 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.

引用

页码：1 / 6

页数：6

共 18 条

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