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Fix xrefs in the LDSVM trainer docs #4940

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Mar 13, 2020
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Fix xrefs in the LDSVM trainer docs #4940

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190e85b
Fix xrefs in the LDSVM trainer docs
natke Mar 13, 2020
e8d1a96
Fix typos in the LDSVM API docs
natke Mar 13, 2020
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8 changes: 4 additions & 4 deletions src/Microsoft.ML.StandardTrainers/LdSvm/LdSvmTrainer.cs
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Expand Up @@ -31,8 +31,8 @@ namespace Microsoft.ML.Trainers
/// </summary>
/// <remarks>
/// <format type="text/markdown"><![CDATA[
/// To create this trainer, use [LdSvm](xref:Microsoft.ML.StandardTrainersCatalog.LdSvm(BinaryClassificationCatalog.BinaryClassificationTrainers, string, string, string, int, int, bool, bool))
/// or [LdSvm(Options)](xref:Microsoft.ML.StandardTrainersCatalog.LdSvm(BinaryClassificationCatalog.BinaryClassificationTrainers, LdSvmTrainer.Options)).
/// To create this trainer, use [LdSvm](xref:Microsoft.ML.StandardTrainersCatalog.LdSvm(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,System.String,System.String,System.String,System.Int32,System.Int32,System.Boolean,System.Boolean))
/// or [LdSvm(Options)](xref:Microsoft.ML.StandardTrainersCatalog.LdSvm(Microsoft.ML.BinaryClassificationCatalog.BinaryClassificationTrainers,Microsoft.ML.Trainers.LdSvmTrainer.Options)).
///
/// [!include[io](~/../docs/samples/docs/api-reference/io-columns-binary-classification-no-prob.md)]
///
Expand All @@ -47,13 +47,13 @@ namespace Microsoft.ML.Trainers
///
/// ### Training Algorithm Details
/// Local Deep SVM (LD-SVM) is a generalization of Localized Multiple Kernel Learning for non-linear SVM. Multiple kernel methods learn a different
/// kernel, and hence a different classifier, for each point in the feature space. The prediction time cost for multiple kernal methods can be prohibitively
/// kernel, and hence a different classifier, for each point in the feature space. The prediction time cost for multiple kernel methods can be prohibitively
/// expensive for large training sets because it is proportional to the number of support vectors, and these grow linearly with the size of the training
/// set. LD-SVM reduces the prediction cost by learning a tree-based local feature embedding that is high dimensional and sparse, efficiently encoding
/// non-linearities. Using LD-SVM, the prediction cost grows logarithmically with the size of the training set, rather than linearly, with a tolerable loss
/// in classification accuracy.
///
/// Local Deep SVM is an implementation of the algorithm decribed in [C. Jose, P. Goyal, P. Aggrwal, and M. Varma, Local Deep
/// Local Deep SVM is an implementation of the algorithm described in [C. Jose, P. Goyal, P. Aggrwal, and M. Varma, Local Deep
/// Kernel Learning for Efficient Non-linear SVM Prediction, ICML, 2013](http://proceedings.mlr.press/v28/jose13.pdf).
///
/// Check the See Also section for links to usage examples.
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