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Learning with Hierarchical Gaussian Kernels
We investigate iterated compositions of weighted sums of Gaussian kernels and provide an interpretation of the construction that shows some similarities with the architectures of deep neural networks.
Towards an Axiomatic Approach to Hierarchical Clustering of Measures
We propose some axioms for hierarchical clustering of probability measures and investigate their ramifications.
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