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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.
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 |
