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English(EN) Conditional KRR: Injecting Unpenalized Features into Kernel Methods with Applications to Kernel Thresholding

条件KRR通过无惩罚特征增强核方法

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员开发了一种称为条件核岭回归（条件KRR）的方法，通过整合无惩罚特征来增强核方法。这种方法类似于在函数类$\mathcal{F}$上执行标准线性回归，然后将KRR应用于剩余的未解释方差。理论分析表明，当回归函数的$\mathcal{F}$-分量显著时，条件KRR的性能可能优于标准KRR，实验结果也支持了这一发现。 AI

影响 引入了一种新颖的核方法，可能在特定的回归任务中提供改进的性能。

排序理由 该集群包含一篇详细介绍新机器学习方法的研究论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 Hugging Face Daily Papers 阅读 →

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条件KRR通过无惩罚特征增强核方法

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报道来源 [1]

  1. Hugging Face Daily Papers TIER_1 English(EN) ·

    条件KRR：将无惩罚特征注入核方法及其在核阈值化中的应用

    Conditionally positive definite (CPD) kernels are defined with respect to a function class $\mathcal{F}$. It is well known that such a kernel $K$ is associated with its native space (defined analogously to an RKHS), which in turn gives rise to a learning method -- called conditio…

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