Name
Abstract | ||
|---|---|---|
When we apply support vector machines (SVM) to multi-class classification, some methods of combining the results of independent SVM for each class haven been used, However, the conventional methods may deteriorates generalization performance when the number of data in each class is small. To solve this problem, we proposed a new method, which uses only one SVM and train it to find some similarity measure between data samples. Through an experiment using real data, we confirm that the proposed method can give better classification performance than the conventional one. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11589990_199 | Australian Conference on Artificial Intelligence |
Keywords | Field | DocType |
conventional method,better classification performance,data sample,generalization performance,multi-class classification,new method,independent svm,class haven,robust svm design,support vector machine,multi class classification | Similitude,Ranking SVM,Similarity measure,Pattern recognition,Computer science,Support vector machine,Artificial intelligence,Machine learning,Statistical analysis,Multiclass classification | Conference |
Volume | ISSN | ISBN |
3809 | 0302-9743 | 3-540-30462-2 |
Citations | PageRank | References |
1 | 0.39 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Minkook Cho | 1 | 6 | 2.55 |
| Hyeyoung Park | 2 | 194 | 32.70 |