Loading...
Please wait, while we are loading the content...
Similar Documents
the Bayesian backfitting relevance vector machine (2004)
| Content Provider | CiteSeerX |
|---|---|
| Author | Vijayakumar, Sethu Schaal, Stefan |
| Description | IN PROCEEDINGS OF THE 21ST INTERNATIONAL CONFERENCE ON MACHINE LEARNING In Proceedings of the 21st International Conference on Machine Learning |
| Abstract | Traditional non-parametric statistical learning techniques are often computationally attractive, but lack the same generalization and model selection abilities as state-of-the-art Bayesian algorithms which, however, are usually computationally prohibitive. This paper makes several important contributions that allow Bayesian learning to scale to more complex, real-world learning scenarios. Firstly, we show that backfitting — a traditional non-parametric, yet highly efficient regression tool — can be derived in a novel formulation within an expectation maximization (EM) framework and thus can finally be given a probabilistic interpretation. Secondly, we show that the general framework of sparse Bayesian learning and in particular the relevance vector machine (RVM), can be derived as a highly efficient algorithm using a Bayesian version of backfitting at its core. As we demonstrate on several regression and classification benchmarks, Bayesian backfitting offers a compelling alternative to current regression methods, especially when the size and dimensionality of the data challenge computational resources. 1. |
| File Format | |
| Publisher Date | 2004-01-01 |
| Access Restriction | Open |
| Subject Keyword | Bayesian Learning Classification Benchmark Real-world Learning Scenario Sparse Bayesian Learning Model Selection Ability Bayesian Version State-of-the-art Bayesian Algorithm Traditional Non-parametric Statistical Learning Technique Relevance Vector Machine Bayesian Backfitting Relevance Vector Machine Several Important Contribution Current Regression Method Probabilistic Interpretation Several Regression Efficient Regression Tool |
| Content Type | Text |
| Resource Type | Proceeding Conference Proceedings Article |
