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)
565 - 568
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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.