Toward a Kernel-Based Uncertainty Decomposition Framework for Data and Models
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DOI: 10.1162/neco_a_01372
Abstract: This letter introduces a new framework for quantifying predictive uncertainty for both data and models that rely on projecting the data into a gaussian reproducing kernel Hilbert space (RKHS) and transforming the data probability density… read more here.
Keywords: data models; uncertainty; toward kernel; framework ... See more keywords