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English(EN) A Statistical Approach to Estimating Sample Size of Machine Learning Models

arXiv新论文探讨机器学习理论、样本量和优化

作者 PulseAugur 编辑部 · [4 个来源] ·

三篇最新的arXiv论文深入探讨了机器学习的理论基础和实际应用。其中一篇论文提出了一个用于估计机器学习模型所需样本量的统计框架，解决了传统功效分析在复杂、非线性模型上面临的挑战。另一篇论文提供了一个数学规划模型及其在机器学习和人工智能中应用的统一分类法，整合了不同子领域零散的文献。第三篇论文概述了过参数化机器学习的理论，探讨了高度复杂的模型如何在拟合嘈杂的训练数据的情况下仍能获得良好的泛化能力，并对传统的偏差-方差权衡提出了质疑。 AI

影响 这些论文推进了机器学习的理论理解，可能影响未来的模型开发和研究方向。

排序理由 该集群包含三篇在arXiv上发表的学术论文，重点关注机器学习的理论方面。

在 arXiv cs.AI 阅读 →

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arXiv新论文探讨机器学习理论、样本量和优化

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该集群包含三篇在arXiv上发表的学术论文，重点关注机器学习的理论方面。
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报道来源 [4]

  1. arXiv cs.AI TIER_1 English(EN) · Dat Phan-Trong, Sunil Gupta, Svetha Venkatesh ·

    一种统计方法用于估计机器学习模型的样本量

    arXiv:2609.09547v1 Announce Type: cross Abstract: Sample size determination for machine learning (ML) prediction models is challenging because conventional power analysis typically requires the predictor-outcome relationship and effect structure to be specified a priori. Nonlinea…

  2. arXiv cs.AI TIER_1 English(EN) · Chaosheng Dong ·

    机器学习与人工智能中的数学规划：模型与应用的统一分类法

    arXiv:2609.07254v1 Announce Type: cross Abstract: Mathematical programming provides a common language for many decisions embedded in modern machine-learning (ML) and artificial-intelligence (AI) systems: selecting retrieval context, routing tokens, allocating inference compute, f…

  3. arXiv stat.ML TIER_1 English(EN) · Yehuda Dar, Vidya Muthukumar, Richard G. Baraniuk ·

    告别偏差-方差权衡？过参数化机器学习理论概述

    arXiv:2109.02355v2 Announce Type: replace Abstract: The last decade of progress in machine learning (ML), especially the deep learning era, has raised a number of scientific questions that challenge the longstanding dogma of the field. One of the most important riddles was the go…

  4. Towards AI TIER_1 English(EN) · Maanitkhanna ·

    机器学习模型内部运作原理

    <figure><img alt="" src="https://cdn-images-1.medium.com/max/612/0*CuBPnLSwkHeBhuXq" /></figure><p>When you think of AI(Artificial Intelligence), what do you see? Most people, upon hearing that name, think of the recently developed LLMs(large language models): ChatGPT, Claude, Ge…

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