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Hypothesis Spaces for Deep Learning
The representer theorems unfold that solutions of these learning models can be expressed as linear combination of a finite number of kernel sessions determined by given data and the reproducing kernel.
Sparse Representer Theorems for Learning in Reproducing Kernel Banach Spaces
Sparsity of a learning solution is a desirable feature in machine learning.
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