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<oembed><version>1.0</version><provider_name>Microsoft Research</provider_name><provider_url>https://www.microsoft.com/en-us/research</provider_url><author_name>Jeff Running</author_name><author_url>https://www.microsoft.com/en-us/research/people/jeffrunn/</author_url><title>Regularization on Discrete Spaces - Microsoft Research</title><type>rich</type><width>600</width><height>338</height><html>&lt;blockquote class="wp-embedded-content" data-secret="4dgYG6dcx8"&gt;&lt;a href="https://www.microsoft.com/en-us/research/publication/regularization-discrete-spaces/"&gt;Regularization on Discrete Spaces&lt;/a&gt;&lt;/blockquote&gt;&lt;iframe sandbox="allow-scripts" security="restricted" src="https://www.microsoft.com/en-us/research/publication/regularization-discrete-spaces/embed/#?secret=4dgYG6dcx8" width="600" height="338" title="&#x201C;Regularization on Discrete Spaces&#x201D; &#x2014; Microsoft Research" data-secret="4dgYG6dcx8" frameborder="0" marginwidth="0" marginheight="0" scrolling="no" class="wp-embedded-content"&gt;&lt;/iframe&gt;&lt;script&gt;
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</html><description>We consider the classification problem on a finite set of objects. Some of them are labeled, and the task is to predict the labels of the remaining unlabeled ones. Such an estimation problem is generally referred to as transductive inference. It is well-known that many meaningful inductive or supervised methods can be derived from a [&hellip;]</description></oembed>
