linear prediction models with graph regularization for web-page categorization
We present a risk minimization formulation for learning from both text and graph structures which is motivated by the problem of collective inference for hypertext document categorization. The method is based on graph regularization formulated as a well-formed convex optimization problem. We present numerical algorithms for our formulation, and show that such combination of local text features and link information can lead to improved predictive accuracy.
prediction model predict graph regularization categorical risk minimal learning text collect collective inference inference hypertext documentation method convex optimization algorithm combinational locality feature linking informal accuracy semi-supervised learning document classification web page graph and relational learning