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* Modified objective for targeted optimization of particular Precision/Recall trade-off:
** 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 trade-off. 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 F-measure of maximum entropy classifiers, RANLP 2013|http://www.aclweb.org/anthology/R13-1027]
* Regularization:
** L1 regularization is often used in practice for sparse models and reducing overfitting. An L1-regularized maxent can also serve as feature selection procedure.