Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1145/1150402.1150510 | KDD |
Keywords | Field | DocType |
collective inference,improved predictive accuracy,hypertext document categorization,graph structure,link information,risk minimization formulation,local text feature,linear prediction model,numerical algorithm,well-formed convex optimization problem,web-page categorization,graph regularization,regularization,semi supervised learning,convex optimization,relational learning,web pages | Document classification,Data mining,Categorization,Hypertext,Semi-supervised learning,Computer science,Inference,Linear prediction,Regularization (mathematics),Artificial intelligence,Convex optimization,Machine learning | Conference |
ISBN | Citations | PageRank |
1-59593-339-5 | 60 | 3.44 |
References | Authors | |
18 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Zhang, Tong | 1 | 7126 | 611.43 |
| Alexandrin Popescul | 2 | 1067 | 104.49 |
| Byron Dom | 3 | 2600 | 825.93 |