A Novel Online Bayes Classifier

Thi Thu Thuy Nguyen, Tien Thanh Nguyen, Xuan Cuong Pham, Alan Wee-Chung Liew, Yongjian Hu, Tiancai Liang, Chang-Tsun Li

Research output: Book chapter/Published conference paperConference paper

6 Citations (Scopus)

Abstract

We present VIGO, a novel online Bayesian classifier for both binary or multiclass problems. In our model, variational inference for multivariate Gaussian distribution technique is exploited to approximate the class conditional probability density functions of data in an online manner.Besides being a conservative learner with a low number of updates compared with many other popular algorithms, VIGO algorithm can be updated in a minibatch of an arbitrary size which makes it robust with noise data. Experiments over a large number of UCI datasets demonstrate the advantage of VIGO with many state-of-the-art methods presented in LIBOL – aprevalent library for online learning algorithms.
Original languageEnglish
Title of host publicationProceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications
Place of PublicationUnited States
PublisherIEEE, Institute of Electrical and Electronics Engineers
Pages1-6
Number of pages6
ISBN (Electronic)9781509028962
ISBN (Print)9781509028979
DOIs
Publication statusPublished - 2016
Event2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) - Mantra on View Hotel, Surfer's Paradise, Australia
Duration: 30 Nov 201602 Dec 2016
http://dicta2016.dictaconference.org/

Conference

Conference2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA)
CountryAustralia
CitySurfer's Paradise
Period30/11/1602/12/16
Internet address

Cite this

Nguyen, T. T. T., Tien Thanh Nguyen, Xuan Cuong Pham, Liew, A. W-C., Hu, Y., Liang, T., & Li, C-T. (2016). A Novel Online Bayes Classifier. In Proceedings of the 2016 International Conference on Digital Image Computing: Techniques and Applications (pp. 1-6). IEEE, Institute of Electrical and Electronics Engineers. https://doi.org/10.1109/DICTA.2016.7796993