More love for rst docs.

Updte svm.rst, fix cross-references, add TODOs, etc.

doc/index.rst

@@ -54,17 +54,18 @@
 
 
 :Features:
-  * **Solid**: :ref:`supervised-learning`: classification, regression
+  * **Solid**: :ref:`supervised-learning`: :ref:`svm`,
+      :ref:`linear_model`.
 
-  * **Work in progress**: :ref:`unsupervised-learning`: :ref:`clustering`, 
-    :ref:`gmm`, manifold learning, :ref:`ICA <ICA>`
+  * **Work in progress**: :ref:`unsupervised-learning`:
+    :ref:`clustering`, :ref:`mixture`, manifold learning, :ref:`ICA
+    <ICA>`, :ref:`gaussian_process`
 
   * **Planed**: Gaussian graphical models, matrix factorization
 
 :License:
   Open source, commercially usable: **BSD license** (3 clause)
 
-
 .. only:: html
 
     .. raw:: html

doc/modules/gaussian_process.rst

-=======================================
-Gaussian Processes for Machine Learning
-=======================================
+
+
+.. _gaussian_process:
+
+==================
+Gaussian Processes
+==================
 
 .. currentmodule:: scikits.learn.gaussian_process
 

doc/modules/linear_model.rst

+
+.. _linear_model:
+
 =========================
 Generalized Linear Models
 =========================
@@ -15,6 +18,8 @@ Across the module, we designate the vector :math:`\beta = (\beta_1,
 ..., \beta_D)` as ``coef_`` and :math:`\beta_0` as ``intercept_``.
 
 
+.. TODO: reference to logistic regression.
+
 .. _ordinary_least_squares:
 
 Ordinary Least Squares (OLS)

doc/modules/mixture.rst

-.. _gmm:
+.. _mixture:
 
 ===================================================
 Gaussian mixture models

doc/modules/svm.rst

+
+.. _svm:
+
 =======================
 Support Vector Machines
 =======================
@@ -28,12 +31,11 @@ The disadvantages of Support Vector Machines include:
     - If the number of features is much greater than the number of
       samples, the method is likely to give poor performances.
 
-    - SVMs do not directly provide probability estimates, so these
-      must be calculated using indirect techniques. In our case, these
-      techniques imply conducting five-fold cross-validation, so
-      performance can suffer.  See method predict_proba for more
-      information.
+    - SVMs do not directly provide probability estimates, these are
+      calculated using five-fold cross-validation, and thus
+      performance can suffer.
 
+.. TODO: add reference to probability estimates
 
 .. _svm_classification:
 
@@ -211,7 +213,8 @@ Tips on Practical Use
     of training errors and support vectors.
 
   * If data for classification are unbalanced (e.g. many positive and
-    few negative), try different penalty parameters C.
+    few negative), set class_weight='auto' and/or try different
+    penalty parameters C.
 
   * Specify larger cache size (keyword cache) for huge problems.
 
@@ -252,13 +255,13 @@ python function or by precomputing the Gram matrix.
 Classifiers with custom kernels behave the same way as any other
 classifiers, except that:
 
-    * Support vectors do no longer represent the vectors, but rather are
-      indices of the support vectors for the training vectors.
+    * Field `support_vectors\_` is now empty, only indices of support
+      vectors are stored in `support_`
 
     * A reference (and not a copy) of the first argument in the fit()
       method is stored for future reference. If that array changes
-      between the use of fit() and predict() you will have
-      unexpected results.
+      between the use of fit() and predict() you will have unexpected
+      results.
 
 
 Using python functions as kernels
@@ -279,12 +282,13 @@ instance that will use that kernel::
     ...
     >>> clf = svm.SVC(kernel=my_kernel)
 
-Passing the gram matrix
-~~~~~~~~~~~~~~~~~~~~~~~
+Using the Gram matrix
+~~~~~~~~~~~~~~~~~~~~~
 
-Set kernel='precomputed' and pass the gram matrix instead of X in the
+Set kernel='precomputed' and pass the Gram matrix instead of X in the
 fit method.
 
+.. TODO: inline example
 
 .. topic:: Examples: