{"@type": "dcat:Dataset", "accessLevel": "public", "accrualPeriodicity": "irregular", "bureauCode": ["026:00"], "contactPoint": {"@type": "vcard:Contact", "fn": "Ashok Srivastava", "hasEmail": "mailto:ashok.n.srivastava@gmail.com"}, "description": "We present a method of generating Mercer Kernels\r\nfrom an ensemble of probabilistic mixture models,\r\nwhere each mixture model is generated from a Bayesian\r\nmixture density estimate. We show how to convert the\r\nensemble estimates into a Mercer Kernel, describe the\r\nproperties of this new kernel function, and give examples\r\nof the performance of this kernel on unsupervised\r\nclustering of synthetic data and also in the domain of\r\nunsupervised multispectral image understanding.", "distribution": [{"@type": "dcat:Distribution", "description": "Probabilistic_Kernels_2003.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/algorithm/Probabilistic_Kernels_2003.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "Probabilistic_Kernels_2003.pdf"}, {"@type": "dcat:Distribution", "description": "Srivastava_ICML_2003.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/algorithm/Srivastava_ICML_2003.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "Srivastava_ICML_2003.pdf"}, {"@type": "dcat:Distribution", "description": "Virtual_Sensors-_Srivastava_2005.pdf", "downloadURL": "https://c3.nasa.gov/dashlink/static/media/algorithm/Virtual_Sensors-_Srivastava_2005.pdf", "format": "PDF", "mediaType": "application/pdf", "title": "Virtual_Sensors-_Srivastava_2005.pdf"}], "identifier": "DASHLINK_118", "issued": "2010-09-10", "keyword": ["ames", "dashlink", "nasa"], "landingPage": "https://c3.nasa.gov/dashlink/resources/118/", "modified": "2025-03-31", "programCode": ["026:029"], "publisher": {"@type": "org:Organization", "name": "Dashlink"}, "title": "Mixture Density Mercer Kernels"}