NDLI: Recursive Bayesian Linear Regression for Adaptive Classification

Content Provider	IEEE Xplore Digital Library
Author	Jen-Tzung Chien Jung-Chun Chen
Copyright Year	1991
Abstract	This paper presents a new recursive Bayesian linear regression (RBLR) algorithm for adaptive pattern classification. This algorithm performs machine learning in nonstationary environments. A classification model is adopted in model training. The initial model parameters are estimated by maximizing the likelihood function of training data. To activate the sequential learning capability, the randomness of the model parameters is properly expressed by the normal-gamma distribution. When new adaptation data are input, sufficient statistics are accumulated to obtain a new normal-gamma distribution as the posterior distribution. Accordingly, a recursive Bayesian algorithm is established to update the hyperparameters. The trajectory of nonstationary environments can be traced to perform the adaptive classification. Such recursive Bayesian models are used to satisfy the requirements of maximal class margin and minimal training error, which are essential in support vector machines (SVMs). In the experiments on the UCI machine learning repository and the FERET facial database, the proposed algorithm outperforms the state-of-art algorithms including SVMs and relevance vector machines (RVMs). The improvement is not only obtained in batch training but also in sequential adaptation. Face classification performance is continuously elevated by adapting to changing facial conditions.
Sponsorship	IEEE Signal Processing Society
Starting Page	565
Ending Page	575
Page Count	11
File Size	832257
File Format	PDF
ISSN	1053587X
Volume Number	57
Issue Number	2
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-02-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Bayesian methods Linear regression Machine learning algorithms Machine learning Pattern classification Classification algorithms Parameter estimation Training data Statistical distributions Support vector machines support vector machine (SVM) Bayesian adaptation face recognition machine learning sequential learning
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering

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4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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