abstract revised for xval_modelselection.ipynb

doc/ipython-notebooks/evaluation/xval_modelselection.ipynb

@@ -1,6 +1,7 @@
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@@ -30,7 +31,7 @@
      "cell_type": "markdown",
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-      "This notebook illustrates how to evaluate prediction algorithms in Shogus using cross-validation, and how to select their parameters using grid-search. Cross-validation estimates  the expected value of a chosen loss function (for example testing error) via dividing data into disjoint partitions and then running the algorithms on a number of combinations for training and testing. Grid-search is a way to compare a number of registered parameters using cross-validation. We demonstrate this on a number of different algorithms within Shogun."
+      "This notebook illustrates evaluation of prediction algorithms in Shogun using <a href=\"http://en.wikipedia.org/wiki/Cross-validation_(statistics)\">cross-validation</a>, and their parameters selection using <a href=\"http://en.wikipedia.org/wiki/Hyperparameter_optimization\">grid-search</a>. We demonstrate this for a toy example on <a href=\"http://en.wikipedia.org/wiki/Binary_classification\">Binary Classification</a> using <a href=\"http://en.wikipedia.org/wiki/Support_vector_machine\">Support Vector machines</a>."
      ]
     },
     {