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We focus on the distribution regression problem: regressing to vector-valued outputs from probability measures.
Theory of reproducing kernels for Hilbert spaces of vector valued functions
George Pedrick · 1957
Earlier work this paper cites.
Probability Measures on Metric Spaces
Kalyanapuram R. Parthasarathy · 1967
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A theory of the learnable
Leslie Valiant · 1984
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Gerald Edgar · 1995
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Charles A. Micchelli and Massimiliano Pontil · 2005
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