Replace dcoumentation with link.: (dotnet#3453)

Author: codemzs

src/Microsoft.ML.StandardTrainers/Standard/SdcaBinary.cs

@@ -1651,17 +1651,7 @@ private protected override SchemaShape.Column[] ComputeSdcaBinaryClassifierSchem
     /// | Is caching required? | No |
     /// | Required NuGet in addition to Microsoft.ML | None |
     ///
-    /// ### Training Algorithm Details
-    /// This trainer is based on the Stochastic Dual Coordinate Ascent (SDCA) method, a state-of-the-art optimization technique for convex objective functions.
-    /// The algorithm can be scaled for use on large out-of-memory data sets due to a semi-asynchronized implementation that supports multi-threading.
-    /// Convergence is underwritten by periodically enforcing synchronization between primal and dual updates in a separate thread.
-    /// Several choices of loss functions are also provided.The SDCA method combines several of the best properties and capabilities of logistic regression and SVM algorithms.
-    /// Note that SDCA is a stochastic and streaming optimization algorithm. The results depends on the order of the training data.
-    /// For reproducible results, it is recommended that one sets 'Shuffle' to False and 'NumThreads' to 1.
-    /// Elastic net regularization can be specified by the 'L2Const' and 'L1Threshold' parameters. Note that the 'L2Const' has an effect on the rate of convergence.
-    /// In general, the larger the 'L2Const', the faster SDCA converges.
-    /// For more information, see: [Scaling Up Stochastic Dual Coordinate Ascent](https://www.microsoft.com/en-us/research/wp-content/uploads/2016/06/main-3.pdf ) and
-    /// [Stochastic Dual Coordinate Ascent Methods for Regularized Loss Minimization](http://www.jmlr.org/papers/volume14/shalev-shwartz13a/shalev-shwartz13a.pdf).
+    /// [!include[algorithm](~/../docs/samples/docs/api-reference/algo-details-sdca.md)].
     /// ]]>
     /// </format>
     /// </remarks>

src/Microsoft.ML.StandardTrainers/StandardTrainersCatalog.cs

@@ -268,7 +268,7 @@ public static SdcaNonCalibratedBinaryTrainer SdcaNonCalibrated(
         }
 
         /// <summary>
-        /// Create <see cref="SdcaNonCalibratedBinaryTrainer"/> using advanced options, which predicts a target using a linear classification model trained over boolean label data.
+        /// Create <see cref="SdcaNonCalibratedBinaryTrainer"/> with advanced options, which predicts a target using a linear classification model trained over boolean label data.
         /// </summary>
         /// <param name="catalog">The binary classification catalog trainer object.</param>
         /// <param name="options">Trainer options.</param>