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	<title>matrix balance regression &#8211; BIOENGINEER.ORG</title>
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		<title>New Two-Stage Regression Framework Tames Multicollinearity and Boosts Prediction Accuracy</title>
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		<pubDate>Wed, 14 Oct 2026 08:35:55 +0000</pubDate>
				<category><![CDATA[Technology]]></category>
		<category><![CDATA[elastic net]]></category>
		<category><![CDATA[generalized additive models]]></category>
		<category><![CDATA[LASSO]]></category>
		<category><![CDATA[Machine Learning]]></category>
		<category><![CDATA[matrix balance regression]]></category>
		<category><![CDATA[multicollinearity]]></category>
		<category><![CDATA[predictive modeling]]></category>
		<category><![CDATA[ridge regression]]></category>
		<category><![CDATA[semi-parametric regression]]></category>
		<category><![CDATA[statistical learning]]></category>
		<category><![CDATA[support vector regression]]></category>
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					<description><![CDATA[Researchers at Persian Gulf University have developed matrix balance regression, a two-stage framework that combines a stabilized parametric model with support vector regression to cut prediction error by roughly 20 percent on real-world datasets.]]></description>
		
		
		
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