modified notebook titles

claesenm · Jul 9, 2015 · 912a43b · 912a43b
1 parent 1e87775
commit 912a43b
Show file tree

Hide file tree

Showing 7 changed files with 15 additions and 12 deletions.
diff --git a/docs/notebooks/notebooks/basic-cross-validation.rst b/docs/notebooks/notebooks/basic-cross-validation.rst
@@ -1,6 +1,6 @@
 
-Cross-validation
-================
+Basic: cross-validation
+=======================
 
 This notebook explores the main elements of Optunity's cross-validation
 facilities, including:

diff --git a/docs/notebooks/notebooks/basic-sobol.rst b/docs/notebooks/notebooks/basic-sobol.rst
@@ -1,6 +1,6 @@
 
-Sobol sequences vs. random search
-=================================
+Basic: Sobol sequences
+======================
 
 In this example we will show the difference between a 2-d Sobol sequence
 and sampling uniformly at random in 2 dimensions. We will use the

diff --git a/docs/notebooks/notebooks/opencv-ocr.rst b/docs/notebooks/notebooks/opencv-ocr.rst
@@ -1,4 +1,7 @@
 
+OpenCV: optical character recognition
+=====================================
+
 We will use OpenCV (http://www.opencv.org/) for optical character
 recognition (OCR) using support vector machine (SVM) classifiers. This
 example is based on OpenCV's digit tutorial (available in
@@ -62,7 +65,7 @@ We preprocess the data and make a train/test split.
 
 
 
-.. image:: opencv-ocr_files/output_6_1.png
+.. image:: opencv-ocr_files/output_7_1.png
 
 
 Now, it's time to construct classifiers. We will use a SVM classifier
@@ -108,7 +111,7 @@ We start with an SVM with default parameters, which in this case means:
 
 
 
-.. image:: opencv-ocr_files/output_9_2.png
+.. image:: opencv-ocr_files/output_10_2.png
 
 
 Next, we will construct a model with optimized hyperparameters. First we
@@ -181,7 +184,7 @@ its test set performance.
 
 
 
-.. image:: opencv-ocr_files/output_15_2.png
+.. image:: opencv-ocr_files/output_16_2.png
 
 
 .. code:: python

diff --git a/docs/notebooks/notebooks/opencv-ocr_files/output_10_2.png b/docs/notebooks/notebooks/opencv-ocr_files/output_10_2.png
diff --git a/docs/notebooks/notebooks/opencv-ocr_files/output_16_2.png b/docs/notebooks/notebooks/opencv-ocr_files/output_16_2.png
diff --git a/docs/notebooks/notebooks/opencv-ocr_files/output_7_1.png b/docs/notebooks/notebooks/opencv-ocr_files/output_7_1.png
diff --git a/docs/notebooks/notebooks/sklearn-svc.rst b/docs/notebooks/notebooks/sklearn-svc.rst
@@ -1,15 +1,15 @@
 
-Tuning a scikit-learn SVM classifier
-====================================
+sklearn: SVM classification
+===========================
 
 In this example we will use Optunity to optimize hyperparameters for a
 support vector machine classifier (SVC) in scikit-learn. We will learn a
 model to distinguish digits 8 and 9 in the MNIST data set in two
 settings
 
--  `tune SVM with RBF kernel <#rbf>`__
--  `tune SVM with RBF, polynomial or linear kernel <#all>`__, that is
-   choose the kernel function and its hyperparameters at once
+-  tune SVM with RBF kernel
+-  tune SVM with RBF, polynomial or linear kernel, that is choose the
+   kernel function and its hyperparameters at once
 
 .. code:: python