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Boosting Multiclass Learning with Repeating Codes
| Content Provider | Semantic Scholar |
|---|---|
| Author | Hsu, Chun-Nan |
| Copyright Year | 2006 |
| Abstract | A long-standing goal of machine learning is to build a system which can detect a large number of classes with accuracy and efficiency. Some relationships between classes would become a scale-free network in which we can classify the assigned class very fast. Many available methods for multiclass problems have been proposed in the literatures, such as AdaBoost.ECC [4], AdaBoost.ERP, [7] and JointBoost [12]. However, many of them are inaccurate or time-consuming on training. In this paper, we propose a new algorithm, called AdaBoost.ERC, which combines the approach of Dietterich and Bakiri [2] based on error correcting output codes (ECOC) and Shapire’s boosting algorithm [3] [10]. With advantages of both concepts, our new approach achieves better performance compared to AdaBoost.ECC, AdaBoost.ERP, and JointBoost. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://www.iis.sinica.edu.tw/~chunnan/DOWNLOADS/taai06.pdf |
| Alternate Webpage(s) | http://www.iis.sinica.edu.tw/page/library/TechReport/tr2007/tr07014.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
