
Key
This line was removed.
This word was removed. This word was added.
This line was added.

Changes (1)
View Page History...
* Modified objective for targeted optimization of particular Precision/Recall tradeoff:
** We implemented a weighted likelihood objective that allows for optimizing a specific F_beta, for a given beta, which means that we can specify a desired Precision/ Recall tradeoff. In practice, we can therefore train models that have very high Precision, or very high Recall, at the expense of the complementary measure.
** We implemented a weighted likelihood objective that allows for optimizing a specific F_beta, for a given beta, which means that we can specify a desired Precision/ Recall tradeoff. In practice, we can therefore train models that have very high Precision, or very high Recall, at the expense of the complementary measure.
** Main publication: [Georgi Dimitroff, Laura Tolosi, Borislav Popov and Georgi Georgiev. [Dimitroff et al. Weighted maximum likelihood as a convenient shortcut to optimize the Fmeasure of maximum entropy classiﬁers, RANLP 2013http://www.aclweb.org/anthology/R131027]
* Regularization:
** L1 regularization is often used in practice for sparse models and reducing overfitting. An L1regularized maxent can also serve as feature selection procedure.
** L1 regularization is often used in practice for sparse models and reducing overfitting. An L1regularized maxent can also serve as feature selection procedure.
...