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ARC Code TI: Block-GP: Scalable Gaussian Process Regression
Block GP is a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence.
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Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| accrualPeriodicity | irregular |
| bureauCode |
[ "026:00" ] |
| contactPoint |
{ "fn": "Dennis Koga", "@type": "vcard:Contact", "hasEmail": "mailto:dennis.koga@nasa.gov" } |
| description | Block GP is a Gaussian Process regression framework for multimodal data, that can be an order of magnitude more scalable than existing state-of-the-art nonlinear regression algorithms. The framework builds local Gaussian Processes on semantically meaningful partitions of the data and provides higher prediction accuracy than a single global model with very high confidence. |
| distribution |
[ { "@type": "dcat:Distribution", "format": "TAR", "mediaType": "application/x-tar", "downloadURL": "http://ti.arc.nasa.gov/m/opensource/downloads/BlockGP.tar.gz" } ] |
| identifier | OCIO-Fitara-113 |
| issued | 2015-07-21 |
| keyword |
[ "algorithm", "block-gp", "code-ti", "data", "gaussian", "multimodal", "regression", "scalable" ] |
| landingPage | http://ti.arc.nasa.gov/opensource/projects/block-gp/ |
| modified | 2025-03-31 |
| programCode |
[ "026:046" ] |
| publisher |
{ "name": "Ames Research Center", "@type": "org:Organization" } |
| theme |
[ "Management/Operations" ] |
| title | ARC Code TI: Block-GP: Scalable Gaussian Process Regression |
