Condensed Vector Machines: Learning Fast Machine for Large Data

  title={Condensed Vector Machines: Learning Fast Machine for Large Data},
  author={D. D. Nguyen and K. Matsumoto and Y. Takishima and Kenji Hashimoto},
  journal={IEEE Transactions on Neural Networks},
Scalability is one of the main challenges for kernel-based methods and support vector machines (SVMs). The quadratic demand in memory for storing kernel matrices makes it impossible for training on million-size data. Sophisticated decomposition algorithms have been proposed to efficiently train SVMs using only important examples, which ideally are the final support vectors (SVs). However, the ability of the decomposition method is limited to large-scale applications where the number of SVs is… CONTINUE READING