Fix broken XREF in XML documentation. (#3576)

* Fix broken XREF in XML documentation.

* PR feedback.

Author: codemzs

src/Microsoft.ML.Recommender/MatrixFactorizationTrainer.cs

@@ -88,8 +88,8 @@ namespace Microsoft.ML.Trainers
     ///
     /// This trainer includes a [stochastic gradient method](https://en.wikipedia.org/wiki/Stochastic_gradient_descent) and a [coordinate descent method](https://en.wikipedia.org/wiki/Coordinate_descent) for finding $P$ and $Q$ via minimizing the distance between (non-missing part of) $R$ and its approximation $P^T Q$.
     /// The coordinate descent method included is specifically for one-class matrix factorization where all observed ratings are positive signals (that is, all rating values are 1).
-    /// Notice that the only way to invoke one-class matrix factorization is to assign [one-class squared loss](xref:"Microsoft.ML.Trainers.MatrixFactorizationTrainer.LossFunctionType.SquareLossOneClass")
-    /// to [loss function](Microsoft.ML.Trainers.MatrixFactorizationTrainer.Options.LossFunction)
+    /// Notice that the only way to invoke one-class matrix factorization is to assign [one-class squared loss](xref:Microsoft.ML.Trainers.MatrixFactorizationTrainer.LossFunctionType.SquareLossOneClass)
+    /// to [loss function](xref:Microsoft.ML.Trainers.MatrixFactorizationTrainer.Options.LossFunction)
     /// when calling [MatrixFactorization(Options)](xref:Microsoft.ML.RecommendationCatalog.RecommendationTrainers.MatrixFactorization(Microsoft.ML.Trainers.MatrixFactorizationTrainer.Options)).
     /// See Page 6 and Page 28 [here](https://www.csie.ntu.edu.tw/~cjlin/talks/facebook.pdf) for a brief introduction to standard matrix factorization and one-class matrix factorization.
     /// The [default setting](xref:Microsoft.ML.Trainers.MatrixFactorizationTrainer.LossFunctionType.SquareLossRegression) induces standard matrix factorization.

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

@@ -34,7 +34,7 @@ namespace Microsoft.ML.Trainers
     /// [SdcaMaximumEntropy(Options)](xref:Microsoft.ML.StandardTrainersCatalog.SdcaMaximumEntropy(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,Microsoft.ML.Trainers.SdcaMaximumEntropyMulticlassTrainer.Options)).
     /// To create this trainer for a [loss function](xref:Microsoft.ML.Trainers.ISupportSdcaClassificationLoss) (such as support vector machine's [hinge loss](xref:Microsoft.ML.Trainers.HingeLoss)) of your choice,
     /// use [SdcaNonCalibrated](xref:Microsoft.ML.StandardTrainersCatalog.SdcaNonCalibrated(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,System.String,System.String,System.String,Microsoft.ML.Trainers.ISupportSdcaClassificationLoss,System.Nullable{System.Single},System.Nullable{System.Single},System.Nullable{System.Int32})) or
-    /// [SdcaNonCalibrated(Options)](Microsoft.ML.StandardTrainersCatalog.SdcaNonCalibrated(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer.Options)).
+    /// [SdcaNonCalibrated(Options)](xref:Microsoft.ML.StandardTrainersCatalog.SdcaNonCalibrated(Microsoft.ML.MulticlassClassificationCatalog.MulticlassClassificationTrainers,Microsoft.ML.Trainers.SdcaNonCalibratedMulticlassTrainer.Options)).
     ///
     /// [!include[io](~/../docs/samples/docs/api-reference/io-columns-multiclass-classification.md)]
     ///