arXiv:2604.18916v1 Announce Type: new Abstract: In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be trained error-free, thus acquiring the capability of not making repeated mistakes. The method is applied to 18 MedMNIST biomedical datasets. Except for three datasets, which suffer from the double-labeling problem, all are trained to perfection.
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✦ AI Summary· Claude Sonnet
Computer Science > Artificial Intelligence
[Submitted on 20 Apr 2026]
Error-free Training for MedMNIST Datasets
Bo Deng
In this paper, we introduce a new concept called Artificial Special Intelligence by which Machine Learning models for the classification problem can be trained error-free, thus acquiring the capability of not making repeated mistakes. The method is applied to 18 MedMNIST biomedical datasets. Except for three datasets, which suffer from the double-labeling problem, all are trained to perfection.
Comments: 8 pages, 2 figure, 1 table
Subjects: Artificial Intelligence (cs.AI)
MSC classes: 68T01
Cite as: arXiv:2604.18916 [cs.AI]
(or arXiv:2604.18916v1 [cs.AI] for this version)
https://doi.org/10.48550/arXiv.2604.18916
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Submission history
From: Bo Deng [view email]
[v1] Mon, 20 Apr 2026 23:37:12 UTC (60 KB)
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