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: These models often require large datasets and can be sensitive to "adversarial noise" (small character-level changes that fool the AI).

Post-hoc explanation of black-box classifiers using confident itemsets 113941

The identifier refers to a specific research article titled "Post-hoc explanation of black-box classifiers using confident itemsets" , published in the journal Expert Systems with Applications (Volume 165, March 2021). Key Details of the Research Authors : Milad Moradi and Matthias Samwald. : These models often require large datasets and

: It addresses the "black-box" problem where complex neural networks provide accurate results but lack transparency, which is critical for high-stakes fields like healthcare. Understanding "Deep Text" 113941

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