add: summary of implemented algorithms

docs/source/index.rst

@@ -12,6 +12,18 @@ Welcome to the documentation for modAL!
 
 modAL is an active learning framework for Python3, designed with *modularity, flexibility* and *extensibility* in mind. Built on top of scikit-learn, it allows you to rapidly create active learning workflows with nearly complete freedom. What is more, you can easily replace parts with your custom built solutions, allowing you to design novel algorithms with ease.
 
+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>`_)
+- **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>`_)
+- **stream-based sampling** (`Atlas et al. <https://papers.nips.cc/paper/261-training-connectionist-networks-with-queries-and-selective-sampling.pdf>`_)
+- **active regression** with *max standard deviance* sampling for Gaussian processes or ensemble regressors
+
+
 .. toctree::
    :maxdepth: 1
    :caption: Overview