Incremental conic functions algorithm for large scale classification problems
| Autor: | Ömer Nezih Gerek, Emre Cimen, Gurkan Ozturk |
|---|---|
| Přispěvatelé: | Anadolu Üniversitesi, Mühendislik Fakültesi, Gerek, Ömer Nezih |
| Rok vydání: | 2018 |
| Předmět: |
Mathematical optimization
0211 other engineering and technologies 02 engineering and technology Machine Learning Artificial Intelligence 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Cluster analysis ComputingMethodologies_COMPUTERGRAPHICS Mathematics Mathematical Programming 021103 operations research Applied Mathematics Classification Data point Computational Theory and Mathematics Conic section Signal Processing 020201 artificial intelligence & image processing Computer Vision and Pattern Recognition Polyhedral Conic Functions Statistics Probability and Uncertainty Algorithm Classifier (UML) Conic optimization Data reduction |
| Zdroj: | Digital Signal Processing. 77:187-194 |
| ISSN: | 1051-2004 |
| Popis: | WOS: 000432635500014 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 Anadolu University Scientific Research Projects Commission [1506F499, 1603F122, 1605F524, 1605F435] This paper is supported by Anadolu University Scientific Research Projects Commission, under project numbers 1506F499, 1603F122, 1605F524 and 1605F435. |
| Databáze: | OpenAIRE |
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