Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
A robust minimax approach to classification
The Journal of Machine Learning Research
SVM-Based feature selection of latent semantic features
Pattern Recognition Letters
Pattern Recognition and Machine Learning (Information Science and Statistics)
Pattern Recognition and Machine Learning (Information Science and Statistics)
Working Set Selection Using Second Order Information for Training Support Vector Machines
The Journal of Machine Learning Research
Bayesian face recognition using support vector machine and face clustering
CVPR'04 Proceedings of the 2004 IEEE computer society conference on Computer vision and pattern recognition
A tutorial on particle filters for online nonlinear/non-GaussianBayesian tracking
IEEE Transactions on Signal Processing
Adaptive Bayesian Latent Semantic Analysis
IEEE Transactions on Audio, Speech, and Language Processing
The evidence framework applied to support vector machines
IEEE Transactions on Neural Networks
Incremental training of support vector machines
IEEE Transactions on Neural Networks
Hi-index | 35.68 |
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.