Dual form back propagation on the EM algorithm

Author

Hu, Hong ; Shi, Zhongzhi

Author_Institution

Key Lab. of Intell. Inf. Process., Inst. of Comput. Technol., Beijing, China

fYear

2011

fDate

8-10 Nov. 2011

Firstpage

256

Lastpage

261

Abstract

Vladimir N. Vapnik. (1998) pointed out that maxlikelihood functions in EM algorithms are just a special risk function. Based on this opinion, a novel EM algorithm uses a risk function differ with maxlikelihood functions, in stead, a risk formula based on the least square method is used. The gradient descending approach should be used in such kind approaches. Such kind EM algorithms can estimate the parameters of a random model from both labeled and unlabeled samples, and are suitable for semi-supervised learning.

Keywords

backpropagation; expectation-maximisation algorithm; gradient methods; learning (artificial intelligence); least squares approximations; parameter estimation; EM algorithm; dual form back propagation; gradient descending approach; least square method; maxlikelihood function; parameter estimation; risk formula; semisupervised learning; special risk function; Error analysis; Gaussian distribution; Information processing; Laboratories; Least squares methods; Maximum likelihood estimation; EM; back propagation learning; mixture Gaussian; semi-supervised learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Granular Computing (GrC), 2011 IEEE International Conference on

Conference_Location

Kaohsiung

Print_ISBN

978-1-4577-0372-0

Type

conf

DOI

10.1109/GRC.2011.6122604

Filename

6122604