In praise of laziness: A lazy strategy for web information extraction
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
MetadataShow full item record
Please cite this item using this persistent URLhttp://hdl.handle.net/11693/28222
A large number of Web information extraction algorithms are based on machine learning techniques. For such extraction algorithms, we propose employing a lazy learning strategy to build a specialized model for each test instance to improve the extraction accuracy and avoid the disadvantages of constructing a single general model. © 2012 Springer-Verlag Berlin Heidelberg.
- Conference Paper 2294