Extensions of the Informative Vector Machine

Neil D. Lawrence, John C. Platt, Michael I. Jordan
,  3635:56-87, 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.

Cite this Paper


BibTeX
@InProceedings{pmlr-v-lawrence-extensions05, title = {Extensions of the Informative Vector Machine}, author = {Neil D. Lawrence and John C. Platt and Michael I. Jordan}, pages = {56--87}, year = {}, editor = {}, volume = {3635}, address = {Berlin}, url = {http://inverseprobability.com/publications/lawrence-extensions05.html}, 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.} }
Endnote
%0 Conference Paper %T Extensions of the Informative Vector Machine %A Neil D. Lawrence %A John C. Platt %A Michael I. Jordan %B %C Proceedings of Machine Learning Research %D %E %F pmlr-v-lawrence-extensions05 %I PMLR %J Proceedings of Machine Learning Research %P 56--87 %U http://inverseprobability.com %V %W PMLR %X 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.
RIS
TY - CPAPER TI - Extensions of the Informative Vector Machine AU - Neil D. Lawrence AU - John C. Platt AU - Michael I. Jordan BT - PY - DA - ED - ID - pmlr-v-lawrence-extensions05 PB - PMLR SP - 56 DP - PMLR EP - 87 L1 - UR - http://inverseprobability.com/publications/lawrence-extensions05.html AB - 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. ER -
APA
Lawrence, N.D., Platt, J.C. & Jordan, M.I.. (). Extensions of the Informative Vector Machine. , in PMLR :56-87

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