Merge branch 'live' into master

Author: JRAlexander

dotnet/xml/Microsoft.ML.Runtime.FactorizationMachine/FieldAwareFactorizationMachineTrainer.xml

@@ -29,8 +29,9 @@
         The algorithm is particularly useful for high dimensional datasets which can be very sparse (e.g. click-prediction for advertising systems).
         <para>An advantage of FFM over SVMs is that the training data does not need to be stored in memory, and the coefficients can be optimized directly.
           For a general idea of what Field-aware Factorization Machines are see: <a href="https://www.csie.ntu.edu.tw/~r01922136/slides/ffm.pdf">Field Aware Factorization Machines</a></para><para>See references below for more details. 
-        This trainer is essentially faster the one introduced in [2] because of some implemtation tricks[3].
-        </para><list type="bullet"><item><description><a href="https://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf">Field-aware Factorization Machines for CTR Prediction</a></description></item><item><description><a href="http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf">Adaptive Subgradient Methods for Online Learning and Stochastic Optimization</a></description></item><item><description><a href="https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf">An Improved Stochastic Gradient Method for Training Large-scale Field-aware Factorization Machine.</a></description></item></list></remarks>
+      
+        This trainer is essentially faster than the one introduced in [2] because of some implementation optimizations[3].
+        </para><list type="bullet"><item><description><a href="http://www.csie.ntu.edu.tw/~cjlin/papers/ffm.pdf">Field-aware Factorization Machines for CTR Prediction</a></description></item><item><description><a href="http://jmlr.org/papers/volume12/duchi11a/duchi11a.pdf">Adaptive Subgradient Methods for Online Learning and Stochastic Optimization</a></description></item><item><description><a href="https://github.com/wschin/fast-ffm/blob/master/fast-ffm.pdf">An Improved Stochastic Gradient Method for Training Large-scale Field-aware Factorization Machine.</a></description></item></list></remarks>
   </Docs>
   <Members>
     <Member MemberName=".ctor">
@@ -327,4 +328,4 @@
       </Docs>
     </Member>
   </Members>
-</Type>
+</Type>