add: list of implemented algorithms updated

Author: cosmic-cortex

docs/source/index.rst

@@ -17,6 +17,7 @@ Currently supported active learning strategies are
 - **uncertainty-based sampling:** *least confident* (`Lewis and Catlett <https://www.sciencedirect.com/science/article/pii/B978155860335650026X?via%3Dihub>`_), *max margin* and *max entropy* 
 - **committee-based algorithms:** *vote entropy*, *consensus entropy* and *max disagreement* (`Cohn et al. <http://www.cs.northwestern.edu/~pardo/courses/mmml/papers/active_learning/improving_generalization_with_active_learning_ML94.pdf>`_)
 - **multilabel strategies:** *SVM binary minimum* (`Brinker <https://link.springer.com/chapter/10.1007%2F3-540-31314-1_24>`_), *max loss*, *mean max loss*, (`Li et al. <http://dx.doi.org/10.1109/ICIP.2004.1421535>`_) *MinConfidence*, *MeanConfidence*, *MinScore*, *MeanScore* (`Esuli and Sebastiani <http://dx.doi.org/10.1007/978-3-642-00958-7_12>`_)
+- **expected error reduction:** *binary* and *log loss* (`Roy and McCallum <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.588.5666&rep=rep1&type=pdf>`_)
 - **Bayesian optimization:** *probability of improvement*, *expected improvement* and *upper confidence bound* (`Snoek et al. <https://papers.nips.cc/paper/4522-practical-bayesian-optimization-of-machine-learning-algorithms.pdf>`_)
 - **batch active learning:** *ranked batch-mode sampling* (`Cardoso et al. <https://www.sciencedirect.com/science/article/pii/S0020025516313949>`_)
 - **information density framework** (`McCallum and Nigam <http://www.kamalnigam.com/papers/emactive-icml98.pdf>`_)