Hierarchical mixtures of experts and the EM algorithm
Neural Computation
The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning - Special issue on inductive transfer
Kernel Matrix Completion by Semidefinite Programming
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Learning from Incomplete Data
A Framework for Learning Predictive Structures from Multiple Tasks and Unlabeled Data
The Journal of Machine Learning Research
Second Order Cone Programming Approaches for Handling Missing and Uncertain Data
The Journal of Machine Learning Research
Multi-Task Learning for Classification with Dirichlet Process Priors
The Journal of Machine Learning Research
On Classification with Incomplete Data
IEEE Transactions on Pattern Analysis and Machine Intelligence
Quadratically gated mixture of experts for incomplete data classification
Proceedings of the 24th international conference on Machine learning
Adaptive mixtures of local experts
Neural Computation
Learning from incomplete data with infinite imputations
Proceedings of the 25th international conference on Machine learning
An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators
Proceedings of the 25th international conference on Machine learning
Max-margin Classification of Data with Absent Features
The Journal of Machine Learning Research
Collapsed variational Dirichlet process mixture models
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Nonlinear Models Using Dirichlet Process Mixtures
The Journal of Machine Learning Research
UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence
Quasar: resource-efficient and QoS-aware cluster management
Proceedings of the 19th international conference on Architectural support for programming languages and operating systems
| Hi-index | 0.00 |
A non-parametric hierarchical Bayesian framework is developed for designing a classifier, based on a mixture of simple (linear) classifiers. Each simple classifier is termed a local "expert", and the number of experts and their construction are manifested via a Dirichlet process formulation. The simple form of the "experts" allows analytical handling of incomplete data. The model is extended to allow simultaneous design of classifiers on multiple data sets, termed multi-task learning, with this also performed non-parametrically via the Dirichlet process. Fast inference is performed using variational Bayesian (VB) analysis, and example results are presented for several data sets. We also perform inference via Gibbs sampling, to which we compare the VB results.