Conference item
Random tessellation forests
- Abstract:
-
Space partitioning methods such as random forests and the Mondrian process are powerful machine learning methods for multi-dimensional and relational data, and are based on recursively cutting a domain. The flexibility of these methods is often limited by the requirement that the cuts be axis aligned. The Ostomachion process and the self-consistent binary space partitioning-tree process were recently introduced as generalizations of the Mondrian process for space partitioning with non-axis al...
Expand abstract
- Publication status:
- Published
- Peer review status:
- Reviewed (other)
Actions
Access Document
- Files:
-
-
(Accepted manuscript, 1.8MB)
-
- Publication website:
- https://papers.nips.cc/paper/9153-random-tessellation-forests
Authors
Bibliographic Details
- Publisher:
- Conference on Neural Information Processing Systems Publisher's website
- Host title:
- Advances in Neural Information Processing Systems 32 (NIPS 2019)
- Publication date:
- 2019-12-10
- Acceptance date:
- 2019-09-04
- Event title:
- Advances in Neural Information Processing Systems 32
- Event location:
- Vancouver, Canada
- Event start date:
- 2019-12-08
- Event end date:
- 2019-12-14
- ISSN:
-
1049-5258
Item Description
- Keywords:
- Pubs id:
-
1087378
- Local pid:
- pubs:1087378
- Deposit date:
- 2020-02-13
Terms of use
- Copyright holder:
- Ge et al
- Copyright date:
- 2019
- Notes:
- This paper was presented at Advances in Neural Information Processing Systems 32 (NIPS 2019), 8.-14 December 2019, Vancouver, Canada
Metrics
If you are the owner of this record, you can report an update to it here: Report update to this record