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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

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2 results for “OptiNet”

On Error and Compression Rates for Prototype Rulescs.LG

OptiNet achieves near-minimax error rates with compression in Euclidean space.

problem Error and compression rates in non-parametric multiclass classification.

method Compression-based learning rule OptiNet and a novel general compression scheme.

result OptiNet achieves non-trivial compression rates with near-minimax error rates in Euclidean space.

OptiNet compression error rates non-parametric multiclass classification

2022-06-16abs ↗pdf ↗

Universal consistency and rates of convergence of multiclass prototype algorithms in metric spacescs.LG

Prototype rules simplify multiclass classification in metric spaces, achieving consistency and reduced complexity.

problem Multiclass classification in metric spaces, focusing on universal consistency and convergence rates.

method Novel Proto-NN and hybrid rules for multiclass classification in metric spaces, analyzing convergence rates.

result Proto-NN is universally consistent and simpler to implement, with similar computational advantages.

multiclass classification metric spaces universal consistency k-NN Prototype rules

2020-10-01abs ↗pdf ↗