http://rdf.ncbi.nlm.nih.gov/pubchem/patent/CN-103177267-B
Outgoing Links
| Predicate | Object |
|---|---|
| classificationIPCInventive | http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06F15-18 http://rdf.ncbi.nlm.nih.gov/pubchem/patentipc/G06K9-62 |
| filingDate | 2013-04-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| grantDate | 2017-02-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationDate | 2017-02-08-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| publicationNumber | CN-103177267-B |
| titleOfInvention | Support vector machine semi-supervised learning method in time-frequency joint |
| abstract | The invention discloses a support vector machine semi-supervised learning method in time-frequency joint. The method comprises the specific steps as follows: step one, training initial SVW classifiers; step two, searching high-confidence samples using the SVW classifier C1 and the SVW classifier C2 to form a high-confidence sample set S; step three, automatically annotating the samples in the high-confidence sample set, placing in a annotated sample set L of the SVW classifier C; step four, repeatedly training the SVW classifier C using the updated annotated sample set L; step five, judging whether to quit the circulation or continuously iterate according to a stopping rule. The confidence of the sample is judged by uniting the feature spaces of the time domain and frequency domain, the judgment to the sample confidence is more accurate in comparison with the traditional judgment based on the single feature space; since the judgment to the sample confidence is more accurate, the classification performance reduction of the classifier caused by the false annotating can be reduced, and the workload of manual annotation is greatly reduced when the method is used for the training of the SVW classifier. |
| priorityDate | 2013-04-22-04:00^^<http://www.w3.org/2001/XMLSchema#date> |
| type | http://data.epo.org/linked-data/def/patent/Publication |
Incoming Links
| Predicate | Subject |
|---|---|
| isDiscussedBy | http://rdf.ncbi.nlm.nih.gov/pubchem/compound/CID25572 http://rdf.ncbi.nlm.nih.gov/pubchem/substance/SID415713197 |
Total number of triples: 12.