Improving Bayesian network local structure learning via data-driven symmetry correction methods
Learning the structure of a Bayesian network (BN) from data is NP-hard. To efficiently handle high-dimensional datasets, many BN local structure learning algorithms are proposed. These learning algorithms can be categorized into two types: constraint-based and score-based. These learning algorithms...
Saved in:
Main Authors: | Zhao, Jianjun, Ho, Shen-Shyang |
---|---|
Other Authors: | School of Computer Science and Engineering |
Format: | Article |
Language: | English |
Published: |
2021
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/151697 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
Similar Items
-
Local and global color symmetries of a symmetrical pattern
by: Abila, Agatha Kristel, et al.
Published: (2019) -
Local and global color symmetries of a symmetrical pattern
by: De Las Peñas, Ma. Louise Antonette N, et al.
Published: (2019) -
Unsupervised data-driven classification of topological gapped systems with symmetries
by: Long, Yang, et al.
Published: (2023) -
Symmetry breaking for semiconductor photocatalysis
by: Di, Jun, et al.
Published: (2024) -
Transformation optics and hidden symmetries
by: Kraft, Matthias, et al.
Published: (2016)