Name
Abstract | ||
|---|---|---|
During the usual SVM biclassification learning process, the bias is chosen a posteriori as the value halfway between separating hyperplanes. A note on different approaches on the calculation of the bias when SVM is used for multiclassification is provided and empirical experimentation is carried out which shows that the accuracy rate can be improved by using bias formulations, although no single formulation stands out as providing better performance. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/TNN.2007.914138 | IEEE Transactions on Neural Networks |
Keywords | Field | DocType |
empirical experimentation,bias formulation,different approach,accuracy rate,single formulation,usual svm biclassification,better performance,value halfway,bias,computer science,computer simulation,learning artificial intelligence,support vector machine,government,support vector machines | Pattern recognition,Computer science,Support vector machine,A priori and a posteriori,Artificial intelligence,Hyperplane,Artificial neural network,Machine learning,Support vector machine classification,Statistical analysis | Journal |
Volume | Issue | ISSN |
19 | 4 | 1045-9227 |
Citations | PageRank | References |
19 | 0.77 | 5 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| L. Gonzalez-Abril | 1 | 153 | 8.48 |
| C. Angulo | 2 | 93 | 5.82 |
| F. Velasco | 3 | 106 | 5.83 |
| J A Ortega | 4 | 19 | 0.77 |