ICML2008/Nonextensive Entropic Kernels
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< ICML2008
André Martins
- Kernels are functions from pairs of data points to real values (Kernel : Point*Point -> Real). They are useful in density estimation and can serve to define similarity measures.
- Instead of points, sometimes we define kernel between distributions (e.g. bag-of-words).
- One entropic kernel is defined based on the symmetrized KL divergence.
- a more general family of entropies is Tsallis entropies
- improves performance in some tasks
