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Updated: Jul 2, 2025

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在语音,语言和听觉科学中实现通用化的机器学习模型:估计样本大小和减少过度装配

Hamzeh Ghasemzadeh1,2,3, Robert E Hillman1,2,4,5, Daryush D Mehta1,2,4,5

  • 1Center for Laryngeal Surgery and Voice Rehabilitation, Massachusetts General Hospital, Boston.

Journal of speech, language, and hearing research : JSLHR
|February 22, 2024
PubMed
概括

嵌套的k-fold交叉验证在语音,语言和听觉科学中提供了比单个分割更强大的机器学习 (ML) 结果. 这种方法提高了统计能力和信心,减少了对可靠的ML研究设计的样本大小需求.

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