Name
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 - a prevalent library for online learning algorithms. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/DICTA.2016.7796993 | 2016 International Conference on Digital Image Computing: Techniques and Applications (DICTA) |
Keywords | Field | DocType |
online Bayes classifier,VIGO algorithm,variational inference,multivariate Gaussian distribution,probability density function,online learning | Data modeling,Data mining,Computer science,Multivariate normal distribution,Artificial intelligence,Binary number,Approximation algorithm,Naive Bayes classifier,Pattern recognition,Inference,Statistical classification,Bayes classifier,Machine learning | Conference |
ISBN | Citations | PageRank |
978-1-5090-2897-9 | 0 | 0.34 |
References | Authors | |
5 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thi Thu Thuy Nguyen | 1 | 0 | 0.68 |
| Tien Thanh Nguyen | 2 | 79 | 12.55 |
| Xuan Cuong Pham | 3 | 54 | 4.75 |
| Alan Wee-Chung Liew | 4 | 799 | 61.54 |
| Yongjian Hu | 5 | 2 | 3.07 |
| Tiancai Liang | 6 | 1 | 1.39 |
| Chang-Tsun Li | 7 | 937 | 72.14 |