This paper introduces new concentration inequalities for error probabilities within the Empirical Risk Principle (ERP) in statistical learning. These inequalities establish non-asymptotic high-confidence lower and upper bounds for the minimal risk, relaxing the traditional boundedness condition on the empirical risk function to Gaussian or exponential integrability. The confidence of the lower bound is independent of training parameters and input dimensions, enabling efficient detection of learning machine deficiencies. The upper bound's confidence is high when the sample size significantly exceeds the parameter set's box dimension in the Orlicz metric. AI
IMPACT Provides theoretical underpinnings for more robust statistical learning models, potentially improving AI system reliability.
RANK_REASON Academic paper published on arXiv detailing theoretical advancements in statistical learning. [lever_c_demoted from research: ic=1 ai=1.0]
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