  Within the context of studies for novel measurement solutions for future
particle physics experiments, we developed a performant kNN-based regressor to
infer the energy of highly-relativistic muons from the pattern of their
radiation losses in a dense and granular calorimeter. The regressor is based on
a pool of weak kNN learners, which learn by adapting weights and biases to each
training event through stochastic gradient descent. The effective number of
parameters optimized by the procedure is in the 60 millions range, thus
comparable to that of large deep learning architectures. We test the
performance of the regressor on the considered application by comparing it to
that of several machine learning algorithms, showing comparable accuracy to
that achieved by boosted decision trees and neural networks.
