Introduces Boltzmann margin to prove near-exponential convergence rates for kNN classification.
arXiv preprint arXiv:2004.11154 , year=
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I-BBS recovers latent manifold dimension d and geometry from ambient distance matrices via two noise-stable integer signatures: top non-Perron multiplet multiplicity and a parameter-free shrinkage law.
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Near-Exponential Convergence Rates for kNN Classification based on Boltzmann Margin
Introduces Boltzmann margin to prove near-exponential convergence rates for kNN classification.
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I-BBS: Coordinate-Free Inference of Latent Sub-Manifolds Using Random Distance Matrix Theory
I-BBS recovers latent manifold dimension d and geometry from ambient distance matrices via two noise-stable integer signatures: top non-Perron multiplet multiplicity and a parameter-free shrinkage law.