Learning Algorithm of Boltzmann Machine Based on Spatial Monte Carlo Integration Method
-
- Muneki Yasuda
- Graduate School of Science and Engineering, Yamagata University, Yamagata 992-8510, Japan
書誌事項
- 公開日
- 2018-04-04
- 資源種別
- journal article
- 権利情報
-
- https://creativecommons.org/licenses/by/4.0/
- DOI
-
- 10.3390/a11040042
- 公開者
- MDPI AG
説明
<jats:p>The machine learning techniques for Markov random fields are fundamental in various fields involving pattern recognition, image processing, sparse modeling, and earth science, and a Boltzmann machine is one of the most important models in Markov random fields. However, the inference and learning problems in the Boltzmann machine are NP-hard. The investigation of an effective learning algorithm for the Boltzmann machine is one of the most important challenges in the field of statistical machine learning. In this paper, we study Boltzmann machine learning based on the (first-order) spatial Monte Carlo integration method, referred to as the 1-SMCI learning method, which was proposed in the author’s previous paper. In the first part of this paper, we compare the method with the maximum pseudo-likelihood estimation (MPLE) method using a theoretical and a numerical approaches, and show the 1-SMCI learning method is more effective than the MPLE. In the latter part, we compare the 1-SMCI learning method with other effective methods, ratio matching and minimum probability flow, using a numerical experiment, and show the 1-SMCI learning method outperforms them.</jats:p>
収録刊行物
-
- Algorithms
-
Algorithms 11 (4), 42-, 2018-04-04
MDPI AG
- Tweet
キーワード
詳細情報 詳細情報について
-
- CRID
- 1360004239471228672
-
- ISSN
- 19994893
-
- 資料種別
- journal article
-
- データソース種別
-
- Crossref
- KAKEN
- OpenAIRE
