On Representing and Generating Kernels by Fuzzy Equivalence Relations

@article{Moser2006OnRA,
  title={On Representing and Generating Kernels by Fuzzy Equivalence Relations},
  author={Bernhard Moser},
  journal={Journal of Machine Learning Research},
  year={2006},
  volume={6},
  pages={2603-2620}
}
Kernels are two-placed functions that can be interpreted as inner products in some Hilbert space. It is this property which makes kernels predestinated to carry linear models of learning, optimization or classification strategies over to non-linear variants. Following this idea, various kernel-based methods like support vector machines or kernel principal component analysis have been conceived which prove to be successful for machine learning, data mining and computer vision applications. When… CONTINUE READING