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Last edited on
Apr 18, 2024 by JJ
.
Thesis:
Bachelor in Mathematics

Author:
Bianca Neubert

Title:
Minimax classification with the nearest neighbor rule

Supervisor:
Jan JOHANNES

Abstract:
In this thesis we consider binary classification with the Nearest Neighbor Rule. We use the minimax approach to evaluate its prediction ability in the finite dimensional case. Deriving the same rate both as an lower bound for the minimax risk and as an upper bound for the maximum risk of the Nearest Neighbor Rule, we can conclude uniform consistency. A short simulation study at the end of the thesis illustrates the discussion.

References:
Gadat, Klein and Marteau. Classification with the nearest neighbor rule in general finite dimensional spaces: necessary and sufficient conditions., Technical report, arXiv:1411.0894, 2014.