Name
Abstract | ||
|---|---|---|
The well-known MinOver algorithm is a simple modification of the perceptron algorithm and provides the maximum margin classifier without a bias in linearly separable two class classification problems. DoubleMinOver as a slight modification of MinOver is introduced, which now includes a bias. It is shown how this simple and iterative procedure can be extended to SoftDoubleMinOver for classification with soft margins and with kernels. On benchmarks the extremely simple SoftDoubleMinOver algorithm achieves the same classification performance with the same computational effort as sophisticated Support-Vector-Machine software. |
Year | DOI | Venue |
|---|---|---|
2005 | 10.1007/11550907_48 | ICANN (2) |
Keywords | Field | DocType |
maximum margin classification,slight modification,simple softdoubleminover algorithm,class classification problem,computational effort,simple procedure,simple modification,linearly separable,iterative procedure,perceptron algorithm,well-known minover algorithm,classification performance,support vector machine | Linear separability,Computer science,Software,Artificial intelligence,Formal methods,Artificial neural network,Pattern recognition,Iterative method,Support vector machine,Algorithm,Margin classifier,Perceptron,Machine learning | Conference |
Volume | ISSN | ISBN |
3697 | 0302-9743 | 3-540-28755-8 |
Citations | PageRank | References |
6 | 0.77 | 10 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Thomas Martinetz | 1 | 1462 | 231.48 |
| Kai Labusch | 2 | 113 | 8.50 |
| Daniel Schneegass | 3 | 71 | 7.15 |