Adaptive Kernel Value Caching for SVM Training

  title={Adaptive Kernel Value Caching for SVM Training},
  author={Q. Li and Zeyi Wen and Bingsheng He},
  journal={IEEE Transactions on Neural Networks and Learning Systems},
  • Q. Li, Zeyi Wen, Bingsheng He
  • Published 8 November 2019
  • Computer Science
  • IEEE Transactions on Neural Networks and Learning Systems
Support vector machines (SVMs) can solve structured multioutput learning problems such as multilabel classification, multiclass classification, and vector regression. SVM training is expensive, especially for large and high-dimensional data sets. The bottleneck of the SVM training often lies in the kernel value computation. In many real-world problems, the same kernel values are used in many iterations during the training, which makes the caching of kernel values potentially useful. The… 
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