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Davis King 2010-05-16 18:54:18 +00:00
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@ -560,7 +560,7 @@ Davis E. King. <a href="http://www.jmlr.org/papers/volume10/king09a/king09a.pdf"
In the above setting, all the training data consists of labeled samples. In the above setting, all the training data consists of labeled samples.
However, it would be nice to be able to benefit from unlabeled data. However, it would be nice to be able to benefit from unlabeled data.
The idea of manifold regularization is to extract useful information from The idea of manifold regularization is to extract useful information from
unlabeled data by defining which data samples are "close" to each other unlabeled data by first defining which data samples are "close" to each other
(perhaps by using their 3 <a href="#find_k_nearest_neighbors">nearest neighbors</a>) (perhaps by using their 3 <a href="#find_k_nearest_neighbors">nearest neighbors</a>)
and then adding a term to and then adding a term to
the loss function that penalizes any decision rule which produces the loss function that penalizes any decision rule which produces