[shark] 04/79: typos

[Ghislain Vaillant]

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ghisvail-guest pushed a commit to branch master
in repository shark.

commit bd472972b60a8dc95456f47c3a41551973726a5d
Author: Christian Igel <igel at diku.dk>
Date:   Mon Oct 26 14:28:15 2015 +0100

    typos
---
 .../rest_sources/tutorials/concepts/library_design/kernels.rst        | 4 +---
 .../rest_sources/tutorials/concepts/library_design/models.rst         | 4 ++--
 2 files changed, 3 insertions(+), 5 deletions(-)

diff --git a/doc/sphinx_pages/rest_sources/tutorials/concepts/library_design/kernels.rst b/doc/sphinx_pages/rest_sources/tutorials/concepts/library_design/kernels.rst
index d730f80..2e31b6b 100644
--- a/doc/sphinx_pages/rest_sources/tutorials/concepts/library_design/kernels.rst
+++ b/doc/sphinx_pages/rest_sources/tutorials/concepts/library_design/kernels.rst
@@ -245,9 +245,7 @@ Some Kernels are differentiable with respect to their parameters. This can for e
 be exploited in gradient-based optimization of these parameters, which in turn amounts
 to a computationally efficient way of finding a suitable space :math:`\mathcal H` in which
 to solve a given learning problem. Further, if the input space is differentiable as well,
-even the derivative with respect to the inputs can be computed. This is currently
-not often used within Shark aside from certain approximation schemes as for
-example the :doxy:`SvmApproximation`.
+even the derivative with respect to the inputs can be computed. 
 
 The derivatives are weighted as outlined in :doc:`../optimization/conventions_derivatives`.
 The parameter derivative is a weighted sum of the derivatives of all elements of the block
diff --git a/doc/sphinx_pages/rest_sources/tutorials/concepts/library_design/models.rst b/doc/sphinx_pages/rest_sources/tutorials/concepts/library_design/models.rst
index d1c5122..be834ec 100644
--- a/doc/sphinx_pages/rest_sources/tutorials/concepts/library_design/models.rst
+++ b/doc/sphinx_pages/rest_sources/tutorials/concepts/library_design/models.rst
@@ -192,7 +192,7 @@ respect to its parameters thus looks like this::
 
 There are a few more methods which result from the fact that AbstractModel
 implements several higher-level interfaces, namely :doxy:`IParameterizable`,
-:doxy:`IConfigurable`, :doxy:`INameable`, and :doxy:`ISerializable`. For
+:doxy:`INameable`, and :doxy:`ISerializable`. For
 example, models are parameterizable and serialized to store results:
 
 
@@ -232,7 +232,7 @@ Model                      Description
                            weighted sum of the discretized activation
 :doxy:`RNNet`              Recurrent neural network for sequences
 :doxy:`OnlineRNNet`        Recurrent neural network for online learning
-:doxy:`KernelExpansion`    linear combination of outputs of :doxy:`AbstractKernelFunction <Kernel>`, given
+:doxy:`KernelExpansion`    linear combination of outputs of :doxy:`AbstractKernelFunction`, given
                            points of a dataset and the point to be evaluated (input point)
 ========================   ==================================================================================
 

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