文件名称:ADL32-Lecture03-Report1.rar
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CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4 - 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.,CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4- 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4 - 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.,CRFsuite: a fast implementation of Conditional Random Fields (CRFs)
CRFSuite is an implementation of Conditional Random Fields (CRFs) for labeling sequential data. The first priority of this software is to train and use CRF models as fast as possible even at the expense of its memory space and code generality. CRFsuite runs 5.4- 61.8 times faster than C++ implementations for training. CRFsuite supports parameter estimation with L1 regularization (Laplacian prior) using Orthant-Wise Limited-memory Quasi-Newton (OW-LQN) method and L2 regularization (Gaussian prior) using Limited-memory BFGS (L-BFGS) method.
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