TY - GEN
T1 - Linear prediction models with graph regularization for Web-page categorization
AU - Zhang, Tong
AU - Popescul, Alexandrin
AU - Dom, Byron
PY - 2006
Y1 - 2006
N2 - 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.
AB - 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.
KW - Collective inference
KW - Document classification
KW - Graph and relational learning
KW - Regularization
KW - Semi-supervised learning
UR - https://www.scopus.com/pages/publications/33749580724
UR - https://www.scopus.com/pages/publications/33749580724#tab=citedBy
U2 - 10.1145/1150402.1150510
DO - 10.1145/1150402.1150510
M3 - Conference contribution
AN - SCOPUS:33749580724
SN - 1595933395
SN - 9781595933393
T3 - Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
SP - 821
EP - 826
BT - KDD 2006
PB - Association for Computing Machinery
T2 - KDD 2006: 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
Y2 - 20 August 2006 through 23 August 2006
ER -