Analysis of the Max-Min Hill-Climbing Algorithm

DOI

10.2991/tlicsc-18.2018.82How to use a DOI?

Keywords

Bayesian networks; Max-Min Hill-Climbing (MMHC).

Abstract

The Bayesian network is one of the effective tools for study of knowledge representation and casual reasoning in the conditions of uncertain in the field of Artificial Intelligence. How to construct a Bayesian network structure from data have become a hot point in recent study. Max-Min Hill-Climbing (MMHC) algorithm is a newly Bayesian network structure learning algorithm. After a lot of simulation experiments, it has been corroborated that MMHC outperforms on average and in terms of various metrics several prototypical and state-of-the-art algorithms.

Copyright

© 2018, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - CONF
AU  - Yue Wang
PY  - 2018/12
DA  - 2018/12
TI  - Analysis of the Max-Min Hill-Climbing Algorithm
BT  - Proceedings of the 2018 International Conference on Transportation & Logistics, Information & Communication, Smart City (TLICSC 2018)
PB  - Atlantis Press
SP  - 509
EP  - 511
SN  - 1951-6851
UR  - https://doi.org/10.2991/tlicsc-18.2018.82
DO  - 10.2991/tlicsc-18.2018.82
ID  - Wang2018/12
ER  -