{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Dennis Koga", "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", "downloadURL": "http://ti.arc.nasa.gov/m/opensource/downloads/BlockGP.tar.gz", "format": "TAR", "mediaType": "application/x-tar"}], "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": {"@type": "org:Organization", "name": "Ames Research Center"}, "theme": ["Management/Operations"], "title": "ARC Code TI: Block-GP: Scalable Gaussian Process Regression"}