@@ -156,7 +156,8 @@ A well-known classifier supporting non-linear classification via kernels. The im
This algorithm provides only probabilities.
\par Linear Discriminant Analysis (LDA)
A simple and fast linear classifier. For description, see any major textbook on Machine Learning or Statistics (e.g. Duda, Hart & Stork, or Hastie, Tibshirani & Friedman). This algorithm can be used with a regularized covariance matrix.
A simple and fast linear classifier. For description, see any major textbook on Machine Learning or Statistics (e.g. Duda, Hart & Stork, or Hastie, Tibshirani & Friedman). This algorithm can be used with a regularized covariance matrix
according to a method proposed by Ledoit & Wolf: "A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices", 2004..
The Linear Discriminant Analysis has the following option.
\par
\li Use shrinkage: Use a classic or a regularized covariance matrix.
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@@ -168,7 +169,7 @@ This algorithm provides only probabilities.
\par
Note that setting shrinkage to 0 should get you the regular LDA behavior. If you additionally force the covariance to be diagonal, you should get a model resembling the Naive Bayes classifier.
\par
This algorithm provides both hyperplan distance and probabilities.
This algorithm provides both hyperplane distance and probabilities.