Online Learning for URL classification
نویسنده
چکیده
This work consists of a review of online algorithms for URL classification followed by some extensions and tweaking of these methods to make them efficient in terms of computational time and memory. We found out that the trade-off in error for these extensions are fairly comparable for some of these algorithms. We also applied two more kernel based methods namely Forgetron and Projectron.
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تاریخ انتشار 2009