Recurrent GraphNeural NetworkswithSet-BasedAggregation
研究使用基于集合的聚合方法的循环图神经网络，确定了从网络参数验证逻辑公式的充分条件，实现网络与特定模态μ-演算片段之间的有效双向等价。
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The Art of Misclassification: Too Many Classes, Not Enough Points
This paper addresses the inherent difficulty of classification under high-class-count, low-sample-size regimes. We propose an information-theoretic “class separability” metric grounded in entropy, which formally characterizes the irreducible inter-class overlap and uncertainty intrinsic to a dataset in feature space. Unlike prior measures, our metric is model-agnostic and sample-size-independent, enabling derivation of a fundamental theoretical upper bound on classification accuracy—i.e., a performance ceiling that no classifier can surpass. Leveraging entropy analysis and uncertainty modeling, we establish a tight generalization bound and empirically validate that this bound aligns closely with human perception of ambiguous decision boundaries. Our core contribution is the formal definition and quantification of the intrinsic solvability of a classification task, thereby providing a principled theoretical benchmark for algorithm design, model selection, and dataset evaluation.
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Recurrent GraphNeural NetworkswithSet-BasedAggregation
研究使用基于集合的聚合方法的循环图神经网络，确定了从网络参数验证逻辑公式的充分条件，实现网络与特定模态μ-演算片段之间的有效双向等价。
0 citationsRead paper
The Art of Misclassification: Too Many Classes, Not Enough Points
This paper addresses the inherent difficulty of classification under high-class-count, low-sample-size regimes. We propose an information-theoretic “class separability” metric grounded in entropy, which formally characterizes the irreducible inter-class overlap and uncertainty intrinsic to a dataset in feature space. Unlike prior measures, our metric is model-agnostic and sample-size-independent, enabling derivation of a fundamental theoretical upper bound on classification accuracy—i.e., a performance ceiling that no classifier can surpass. Leveraging entropy analysis and uncertainty modeling, we establish a tight generalization bound and empirically validate that this bound aligns closely with human perception of ambiguous decision boundaries. Our core contribution is the formal definition and quantification of the intrinsic solvability of a classification task, thereby providing a principled theoretical benchmark for algorithm design, model selection, and dataset evaluation.
0 citationsRead paper