ausblenden:
Schlagwörter:
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Zusammenfassung:
We believe that the cluster assumption is key
to successful semi-supervised learning.
Based on this, we propose three semi-supervised algorithms:
1. deriving graph-based distances that emphazise low density regions
between clusters, followed by training a standard SVM;
2. optimizing the Transductive SVM objective function,
which places the decision boundary in low density regions,
by gradient descent;
3. combining the first two to make
maximum use of the cluster assumption.
We compare with state of the art algorithms and demonstrate superior accuracy for the latter two methods.
