More specifically , we use LIBSVM ( Chang and Lin , 2001 ) with a quadratic kernel K ( xZ , xj ) = ( - yxT xj + r ) 2 and the built-in one-versus-all strategy for multi-class classification .
libsvm a library for support vector machines software available at httpwwwcsientuedutw cjlinlibsvm
C-C Chang C-J Lin
We use support vector machines3 to predict the next parser action from a feature vector representing the history . More specifically , we use LIBSVM ( Chang and Lin , 2001 ) with a quadratic kernel K ( xZ , xj ) = ( - yxT xj + r ) 2 and the built-in one-versus-all strategy for multi-class classification . Symbolic features are
More specifically , we use LIBSVM ( @@CITATION ) with a quadratic kernel K ( xZ , xj ) = ( - yxT xj + r ) 2 and the built-in one-versus-all strategy for multi-class classification .