Statistical Invariants for Classification on Tiny Datasets

We investigate the influence of statistical invariants for classification problems on tiny datasets. We present a review of current state-of-the-art methods for classification, with a brief discussion of the differences and trade-offs between the proposed method and existing classifiers. Then, we lay out the philosophical and mathematical foundations of the statistical theory of learning, incorporating the discovery of statistical invariants. We derive algorithmic implementations for binary, multiclass, and multilabel classification alongside technical details and recommendations for practitioners. We demonstrate the efficacy of the proposed algorithm through comparative studies against state-of-the-art AutoML frameworks on a benchmark suite.