| Predicate |
Object |
| assignee |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentassignee/MD5_dcf8e5b631212c8c73336a698b6b4119 |
| classificationCPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G01N2015-1477 |
| classificationCPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6242
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6252
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-22
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-24
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6215
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-40
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-21375
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V20-64
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-764
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06V10-761
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-00711
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06K9-6267
http://rdf.ncbi.nlm.nih.gov/pubchem/patentcpc/G06F18-21342 |
| classificationIPCAdditional |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G01N15-14 |
| classificationIPCInventive |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06V10-764 |
| filingDate |
2016-10-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate |
2020-11-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| inventor |
http://rdf.ncbi.nlm.nih.gov/pubchem/patentinventor/MD5_fe23a9f14f30c337c353aab296b73f6e |
| publicationDate |
2020-11-24-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber |
US-10846565-B2 |
| titleOfInvention |
Apparatus, method and computer program product for distance estimation between samples |
| abstract |
Apparatus, method, computer program product and computer readable medium are disclosed for distance estimation between samples. The method includes: modeling the distribution of each of two feature vector sets by a non-parametric model; and calculating the distance of the two distributions, wherein a kernel function is used in the non-parametric model, the kernel function is optimized based on labeled training data, the first feature vector set includes a plurality of feature vectors extracted from a sample, and the second feature vector set includes a plurality of feature vectors extracted from another sample. |
| priorityDate |
2016-10-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type |
http://data.epo.org/linked-data/def/patent/Publication |