An Implementation of Training Dual-nu Support Vector Machines
| Autor: | Hong Gunn Chew, Cheng-Chew Lim, R.E. Bogner |
|---|---|
| Rok vydání: | 2005 |
| Předmět: |
Computer Science::Machine Learning
Computer science business.industry Process (computing) Value (computer science) Machine learning computer.software_genre Class (biology) Dual (category theory) Support vector machine Kernel (linear algebra) ComputingMethodologies_PATTERNRECOGNITION Computer Science::Computer Vision and Pattern Recognition Decomposition (computer science) Decomposition method (constraint satisfaction) Artificial intelligence business computer |
| Zdroj: | Applied Optimization ISBN: 0387242546 |
| DOI: | 10.1007/0-387-24255-4_7 |
| Popis: | Dual-ν Support Vector Machine (2ν-SVM) is a SVM extension that reduces the complexity of selecting the right value of the error parameter selection. However, the techniques used for solving the training problem of the original SVM cannot be directly applied to 2ν-SVM. An iterative decomposition method for training this class of SVM is described in this chapter. The training is divided into the initialisation process and the optimisation process, with both processes using similar iterative techniques. Implementation issues, such as caching, which reduces the memory usage and redundant kernel calculations are discussed. |
| Databáze: | OpenAIRE |
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