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Extensions of the Informative Vector Machine
| Content Provider | Microsoft Research |
|---|---|
| Author | Lawrence, Neil Platt, John C. Jordan, Michael I. |
| Copyright Year | 2005 |
| Abstract | The informative vector machine (IVM) is a practical method for Gaussian process regression and classification. The IVM produces a sparse approximation to a Gaussian process by combining assumed density filtering with a heuristic for choosing points based on minimizing posterior entropy. This paper extends IVM in several ways. First, we propose a novel noise model that allows the IVM to be applied to a mixture of labeled and unlabeled data. Second, we use IVM on a block-diagonal covariance matrix, for learning to learn from related tasks. Third, we modify the IVM to incorporate prior knowledge from known invariances. All of these extensions are tested on artificial and real data. |
| Related Links | http://www.springer-ny.com/ |
| Language | English |
| Publisher | Springer-Verlag All copyrights reserved by Springer 2004 |
| Publisher Date | 2005-07-01 |
| Access Restriction | Open |
| Rights Holder | Microsoft Corporation |
| Subject Keyword | Machine learning intelligence |
| Content Type | Text |
| Resource Type | Proceeding |
