Name
Abstract | ||
|---|---|---|
We propose in this work a nested version of the well–known Sequential Minimal Optimization (SMO) algorithm, able to contemplate working sets of larger cardinality for solving Support Vector Machine (SVM) learning problems. Contrary to several other proposals in literature, neither new procedures nor numerical QP optimizations must be implemented, since our proposal exploits the conventional SMO method in its core. Preliminary tests on benchmarking datasets allow to demonstrate the effectiveness of the presented method. |
Year | DOI | Venue |
|---|---|---|
2012 | 10.1007/978-3-642-33266-1_20 | ICANN (2) |
Keywords | Field | DocType |
numerical qp optimizations,support vector machine,conventional smo method,nested version,larger cardinality,nested sequential minimal optimization,sequential minimal optimization,preliminary test,new procedure,benchmarking datasets | Computer science,Support vector machine,Algorithm,Cardinality,Artificial intelligence,Relevance vector machine,Sequential minimal optimization,Benchmarking,Machine learning | Conference |
Citations | PageRank | References |
3 | 0.40 | 10 |
Authors | ||
5 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Alessandro Ghio | 1 | 667 | 35.71 |
| Davide Anguita | 2 | 1001 | 70.58 |
| Luca Oneto | 3 | 830 | 63.22 |
| Sandro Ridella | 4 | 677 | 140.62 |
| Carlotta Schatten | 5 | 70 | 8.73 |