On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochastic-Dynamical Perspective
Machine learning procedures are commonly evaluated in terms of predictive accuracy and computational efficiency. However, their achievable performance is fundamentally constrained by structural properties of the underlying data-generating process, which are formalized in terms of informational bounds. In this work we examine intrinsic limits of data-driven decision systems from an information-theoretic and interaction-based perspective. We analyze minimal achievable error in classification through Fano-type bounds and precision limits in parametric estimation via the Cramér-Rao inequality, emp
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“On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochast”
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“On the Structural Limits of Machine Learning Decision Systems: An Information-Theoretic, Interaction-Based, and Stochast”