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Applying supervised learning methods to new classification tasks requires domain experts to label sufficient training data for the classifier to achieve acceptable performance. It is desirable to mitigate this annotation effort. To this end, a pertinent observation is that instance labels are often an indirect form of supervision; it may be more efficient to impart domain knowledge directly to the model in the form of labeled features. We present a novel algorithm for exploiting such domain knowledge which we call the Constrained Weight Space SVM (CW-SVM). In addition to exploiting binary labeled features, our approach allows domain experts to provide ranked features, and, more generally, to express arbitrary expected relationships between sets of features. Our empirical results show that the CW-SVM outperforms both baseline supervised learning strategies and previously proposed methods for learning with labeled features.

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