Name
Abstract | ||
|---|---|---|
In order to cope with classification problems involving large datasets, we propose a new mathematical programming algorithm by extending the clustering based polyhedral conic functions approach. Despite the high classification efficiency of polyhedral conic functions, the realization previously required a nested implementation of k-means and conic function generation, which has a computational load related to the number of data points. In the proposed algorithm, an efficient data reduction method is employed to the k-means phase prior to the conic function generation step. The new method not only improves the computational efficiency of the successful conic function classifier, but also helps avoiding model over-fitting by giving fewer (but more representative) conic functions. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.dsp.2017.11.010 | Digital Signal Processing |
Keywords | Field | DocType |
Polyhedral conic functions,Mathematical programming,Classification,Machine learning | Data point,Mathematical optimization,Algorithm,Conic optimization,Cluster analysis,Classifier (linguistics),Conic section,Mathematics,Data reduction | Journal |
Volume | ISSN | Citations |
77 | 1051-2004 | 0 |
PageRank | References | Authors |
0.34 | 9 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Emre Cimen | 1 | 0 | 0.34 |
| G. Ozturk | 2 | 13 | 3.98 |
| Ömer Nezih Gerek | 3 | 118 | 19.51 |