| dc.contributor | 統計系 | |
| dc.creator (作者) | 翁久幸 | zh_TW |
| dc.date (日期) | 2016 | |
| dc.date.accessioned | 17-May-2017 16:31:20 (UTC+8) | - |
| dc.date.available | 17-May-2017 16:31:20 (UTC+8) | - |
| dc.date.issued (上傳時間) | 17-May-2017 16:31:20 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/109732 | - |
| dc.description.abstract (摘要) | 線上(即時)機器學習係指內那些依順序逐個逐個處理資料觀測值的機器學習演算方法，在這種學習方法中，各個觀測值在被處理過後即可刪去，不需保留，因此,線上演算法需要的記憶空間較少。加上這類演算法一般較為簡單，容易執行，對於處理大量且即時的資料具有相當優勢。近年來由於網際網路發達產生許多大量且即時的資料，因而使即時演算法益加受到關注。本研究討論網路產品評比資料之即時統計分析方法,應用於實際資料之情況良好。研究成果可以有實際之應用。 | |
| dc.description.abstract (摘要) | Online learning refers to learning methods that process data one-by-one. Since the data point can be removed after being processed, online methods require less memory and are advantageous when dealing with very large real-time data. This project studies online statistical inference for Internet product ratings data. The proposed method is applied to two real datasets. The results are satisfactory. | |
| dc.format.extent | 399150 bytes | - |
| dc.format.mimetype | application/pdf | - |
| dc.relation (關聯) | MOST 104-2118-M-004-005 | |
| dc.subject (關鍵詞) | 貝氏分析; 線上(即時)機器學習 | |
| dc.subject (關鍵詞) | Bayesian inference; online machine learning | |
| dc.title (題名) | 從即時貝氏學習方法到群組學習 | zh_TW |
| dc.type (資料類型) | report | |
