Name
Abstract | ||
|---|---|---|
Random sampling techniques have been developed for combinatorial optimization problems. In this note, we report an application of one of these techniques for training support vector machines (more precisely, primal-form maximal-margin classifiers) that solve two-group classification problems by using hyperplane classifiers. Through this research, we are aiming (I) to design efficient and theoretically guaranteed support vector machine training algorithms, and (II) to develop systematic and efficient methods for finding "outliers", i.e., examples having an inherent error. |
Year | DOI | Venue |
|---|---|---|
2001 | 10.1007/3-540-45583-3_11 | ALT |
Keywords | Field | DocType |
combinatorial optimization problem,hyperplane classifier,theoretically guaranteed support vector,efficient method,random sampling technique,vector machines,machine training algorithm,inherent error,training support,primal-form maximal-margin classifier,training support vector machine,two-group classification problem,random sampling,support vector machine | Structured support vector machine,Computer science,Random subspace method,Support vector machine,Outlier,Convex hull,Sampling (statistics),Artificial intelligence,Relevance vector machine,Hyperplane,Machine learning | Conference |
ISBN | Citations | PageRank |
3-540-42875-5 | 34 | 2.03 |
References | Authors | |
9 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| José L. Balcázar | 1 | 701 | 62.06 |
| Yang Dai | 2 | 34 | 2.03 |
| Osamu Watanabe | 3 | 960 | 104.55 |