Model-based learning for point pattern data
| dc.contributor.author | Vo, Ba-Ngu | |
| dc.contributor.author | Dam, N. | |
| dc.contributor.author | Phung, D. | |
| dc.contributor.author | Tran, Q. | |
| dc.contributor.author | Vo, B. | |
| dc.date.accessioned | 2018-08-08T04:41:12Z | |
| dc.date.available | 2018-08-08T04:41:12Z | |
| dc.date.created | 2018-08-08T03:50:59Z | |
| dc.date.issued | 2018 | |
| dc.identifier.citation | Vo, B. and Dam, N. and Phung, D. and Tran, Q. and Vo, B. 2018. Model-based learning for point pattern data. Pattern Recognition. 84: pp. 136-151. | |
| dc.identifier.uri | http://hdl.handle.net/20.500.11937/69503 | |
| dc.identifier.doi | 10.1016/j.patcog.2018.07.008 | |
| dc.description.abstract |
This article proposes a framework for model-based point pattern learning using point process theory. Likelihood functions for point pattern data derived from point process theory enable principled yet conceptually transparent extensions of learning tasks, such as classification, novelty detection and clustering, to point pattern data. Furthermore, tractable point pattern models as well as solutions for learning and decision making from point pattern data are developed. | |
| dc.publisher | Elsevier | |
| dc.relation.sponsoredby | http://purl.org/au-research/grants/arc/DP160104662 | |
| dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | |
| dc.title | Model-based learning for point pattern data | |
| dc.type | Journal Article | |
| dcterms.source.volume | 84 | |
| dcterms.source.startPage | 136 | |
| dcterms.source.endPage | 151 | |
| dcterms.source.issn | 0031-3203 | |
| dcterms.source.title | Pattern Recognition | |
| curtin.department | School of Electrical Engineering, Computing and Mathematical Science (EECMS) | |
| curtin.accessStatus | Open access |
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