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English(EN) A family of spectral conjugate gradient algorithms derived by least-squares approximations based on a modified quasi--Newton update with application to a revised robust binary classification model

开发了新的光谱共轭梯度算法,用于优化和分类

研究人员开发了一种新的光谱共轭梯度算法,该算法对经典的Hestenes--Stiefel方法进行了修改。这种新算法旨在保持抗干扰特性,同时确保足够的下降性质。它结合了从更新方案派生的改进的割线方程,从而实现了无记忆的BFGS更新。光谱参数经过调整,以便在最小二乘框架内与BFGS方向对齐,并在优化模型上进行了测试,并将其应用于支持向量机(SVM)的鲁棒二分类模型。 AI

影响 这项研究引入了一种新颖的算法,可以提高机器学习模型的效率和准确性,尤其是在分类任务中。

排序理由 这是一篇详细介绍新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.LG 阅读 →

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开发了新的光谱共轭梯度算法,用于优化和分类

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这是一篇详细介绍新算法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.LG TIER_1 English(EN) · Saman Babaie-Kafaki, Maryam Khoshsimaye-Bargard, Ahmad Mousavi ·

    基于改进拟牛顿更新的最小二乘近似推导的一族谱共轭梯度算法及其在修正鲁棒二分类模型中的应用

    arXiv:2609.13526v1 Announce Type: cross Abstract: We develop a spectral three-term modification of the classic Hestenes--Stiefel conjugate gradient algorithm, preserving its anti-jamming characteristic and, simultaneously, taking care of the sufficient descent property. We discus…