Lingxiao Qu

High-dimensional small-sample learning can achieve robust classification without parameter-heavy deep networks simply by systematically balancing kernel variance, margin, and compactness.

Balanced $k$-shot sampling inadvertently breaks classical small-sample discriminant estimators through an exact algebraic degeneracy, yet even after deriving closed-form repairs, a standard cross-validated logistic probe remains superior on LLM embeddings.