Name
Title | ||
|---|---|---|
MaxMinOver regression: a simple incremental approach for support vector function approximation |
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. In [1] and [2] we presented DoubleMinOver and MaxMinOver as extensions of MinOver which provide the maximal margin solution in the primal and the Support Vector solution in the dual formulation by dememorising non Support Vectors. These two approaches were augmented to soft margins based on the ν-SVM and the C2-SVM. We extended the last approach to SoftDoubleMaxMinOver [3] and finally this method leads to a Support Vector regression algorithm which is as efficient and its implementation as simple as the C2-SoftDoubleMaxMinOver classification algorithm. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11840817_16 | ICANN (1) |
Keywords | Field | DocType |
support vector solution,c2-softdoublemaxminover classification algorithm,support vector regression algorithm,class classification problem,support vector function approximation,simple incremental approach,simple modification,maximum margin classifier,non support vectors,maximal margin solution,perceptron algorithm,maxminover regression,well-known minover algorithm,support vector regression,function approximation,support vector | Linear separability,Function approximation,Iterative method,Regression analysis,Computer science,Support vector machine,Algorithm,Artificial intelligence,Margin classifier,Artificial neural network,Perceptron,Machine learning | Conference |
Volume | ISSN | ISBN |
4131 | 0302-9743 | 3-540-38625-4 |
Citations | PageRank | References |
0 | 0.34 | 11 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Daniel Schneegass | 1 | 71 | 7.15 |
| Kai Labusch | 2 | 113 | 8.50 |
| Thomas Martinetz | 3 | 1462 | 231.48 |