| dc.contributor | 統計系 | en_US |
| dc.creator (作者) | 翁久幸 | zh_TW |
| dc.creator (作者) | Weng, Chiu-Hsing, | en_US |
| dc.date (日期) | 2013-11 | en_US |
| dc.date.accessioned | 4-Jun-2014 12:24:15 (UTC+8) | - |
| dc.date.available | 4-Jun-2014 12:24:15 (UTC+8) | - |
| dc.date.issued (上傳時間) | 4-Jun-2014 12:24:15 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/66451 | - |
| dc.description.abstract (摘要) | 機器學習這門學科是關於設計演算法, 讓計算機得以透過演算法從數據中自動分析學習資料的規律。 很多的機器學習方法根基於機率模型以及貝氏統計的架構。 近似的貝氏推論方法中, 像馬可夫鏈蒙特卡羅這類的隨機模擬向來廣為人知且深受歡迎。 然而, 除了隨機模擬之外, 還有一些確定性的近似方法在許多應用中獲得相當成功。 我們在這篇文章裡將介紹若干確定性近似方法的概念和發展。 | en_US |
| dc.description.abstract (摘要) | Machine learning is a scientific discipline that concerned with designing algorithms to automatically learn complex patterns based on data. Many of the machine learning methods rely on probabilistic models and treat the models in a Bayesian framework. Sampling methods such as Markov Chain Monte Carlo are popular and well known for approximate Bayesian inference. Alternatively, there are deterministic approximation techniques which have been successful in many applications. We present here some of the concepts and developments about deterministic approximation methods. | en_US |
| dc.format.extent | 153811 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.language.iso | en_US | - |
| dc.relation (關聯) | 中國統計學報,52(1),44-58 | en_US |
| dc.subject (關鍵詞) | 近似貝氏推論; 機器學習 | en_US |
| dc.subject (關鍵詞) | Approximate Bayesian inference; machine learning | en_US |
| dc.title (題名) | 機器學習中的近似貝氏推論 | zh_TW |
| dc.title.alternative (其他題名) | An Overview Of Approximate Bayesian Inference In Machine Learning | en_US |
| dc.type (資料類型) | article | en |
