When we run our classifiers on resource-tight environments such as cell-phones , we can use a random feature mixing technique ( Ganchev and Dredze , 2008 ) or a memory-efficient trie implementation based on a succinct data structure ( Jacobson , 1989 ; Delpratt et al. , 2006 ) to reduce required memory usage .
small statistical models by random feature mixing
Kuzman Ganchev Mark Dredze
To speed up classifiers used in a real-time application , we can build fstries incrementally by using feature vectors generated from user inputs . When we run our classifiers on resource-tight environments such as cell-phones , we can use a random feature mixing technique ( Ganchev and Dredze , 2008 ) or a memory-efficient trie implementation based on a succinct data structure ( Jacobson , 1989 ; Delpratt et al. , 2006 ) to reduce required memory usage . We will combine our method with other techniques that provide sparse solutions , for example , kernel methods on a budget ( Dekel and Singer , 2007 ; Dekel et al. , 2008 ; Orabona et al. , 2008 ) or kernel approximation ( surveyed in Kashima et al. ( 2009 ) ) .
When we run our classifiers on resource-tight environments such as cell-phones , we can use a random feature mixing technique ( @@CITATION ) or a memory-efficient trie implementation based on a succinct data structure ( Jacobson , 1989 ; Delpratt et al. , 2006 ) to reduce required memory usage .