The Impacts of Dimension Extension, Postprocess, and Fuzzy Sets to Support Vector Machines on Predicting Ordinal Classes
碩士 === 國立屏東商業技術學院 === 資訊管理系 === 97 === The prediction of ordinal scale data is a long-standing, difficult, and unsolved problem in the machine learning/data mining research. Support Vector Machine (SVM) have demonstrated itself a robust and well-performed algorithm. It has successfully been applie...
| Main Authors: | , |
|---|---|
| Other Authors: | |
| Format: | Others |
| Language: | zh-TW |
| Published: |
2009
|
| Online Access: | http://ndltd.ncl.edu.tw/handle/83288271911324824877 |