| dc.contributor | 統計系 | - |
| dc.creator (作者) | Weng, Ruby C.;Huang, Tzu-kuo;Lin, Chih-jen | - |
| dc.creator (作者) | 翁久幸 | - |
| dc.date (日期) | 2006 | - |
| dc.date.accessioned | 7-Apr-2015 17:02:11 (UTC+8) | - |
| dc.date.available | 7-Apr-2015 17:02:11 (UTC+8) | - |
| dc.date.issued (上傳時間) | 7-Apr-2015 17:02:11 (UTC+8) | - |
| dc.identifier.uri (URI) | http://nccur.lib.nccu.edu.tw/handle/140.119/74371 | - |
| dc.description.abstract (摘要) | The Bradley-Terry model for obtaining individual skill from paired comparisons has been popular in many areas. In machine learning, this model is related to multi-class probability estimates by coupling all pairwise classification results. Error correcting output codes (ECOC) are a general framework to decompose a multi-class problem to several binary problems. To obtain probability estimates under this framework, this paper introduces a generalized Bradley-Terry model in which paired individual comparisons are extended to paired team comparisons. We propose a simple algorithm with convergence proofs to solve the model and obtain individual skill. Experiments on synthetic and real data demonstrate that the algorithm is useful for obtaining multi-class probability estimates. Moreover, we discuss four extensions of the proposed model: 1) weighted individual skill, 2) home-field advantage, 3) ties, and 4) comparisons with more than two teams. | - |
| dc.relation (關聯) | Journal of Machine Learning Research - JMLR , vol. 7, pp. 85-115 | - |
| dc.subject (關鍵詞) | Bradley-Terry model; probability estimates; error correcting output codes; support vector machines | - |
| dc.title (題名) | Generalized Bradley-Terry Models and Multi-Class Probability Estimates | - |
| dc.type (資料類型) | article | en |
