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	<title>risk estimation &#8211; BIOENGINEER.ORG</title>
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		<title>Eigenvector Alignment, Not Training Error, Predicts How Kernel Machines Generalize</title>
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		<pubDate>Wed, 07 Oct 2026 21:53:13 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[eigenvalues]]></category>
		<category><![CDATA[eigenvectors]]></category>
		<category><![CDATA[generalization]]></category>
		<category><![CDATA[kernel alignment]]></category>
		<category><![CDATA[kernel ridge regression]]></category>
		<category><![CDATA[learning theory]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[matrix perturbation theory]]></category>
		<category><![CDATA[overfitting]]></category>
		<category><![CDATA[regularization]]></category>
		<category><![CDATA[risk estimation]]></category>
		<category><![CDATA[spectral bias]]></category>
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					<description><![CDATA[A new theoretical study shows that in kernel ridge regression, generalization hinges on the alignment between learning targets and the eigenvectors of the kernel matrix, while training error is nearly uninformative.]]></description>
		
		
		
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