Update doc.

AUTHORS.rst

@@ -50,7 +50,8 @@ People
 
   * `Gael Varoquaux <http://gael-varoquaux.info/blog/>`_
 
-  * Jake VanderPlas contributed the BallTree module in February 2010.
+  * `Jake VanderPlas <http://www.astro.washington.edu/users/vanderplas/>`_ 
+    contributed the BallTree module in February 2010.
 
   * `Alexandre Gramfort
     <http://www-sop.inria.fr/members/Alexandre.Gramfort/index.fr.html>`_
@@ -75,8 +76,8 @@ People
     fixes.
 
   * `Mathieu Blondel <http://mblondel.org/journal>`_ joined the
-    projectin September 2010 and worked on the sparse matrix support,
-    text feature extraction and bug fixes.
+    project in September 2010 and has worked since on the sparse
+    matrix support, text feature extraction and general bug fixes.
 
   * `Peter Prettenhofer
     <http://sites.google.com/site/peterprettenhofer/>`_ joined the

doc/modules/linear_model.rst

@@ -127,7 +127,7 @@ this reason, the Lasso and its variants are fundamental to the field
 of compressed sensing.
 
 This implementation uses coordinate descent as the algorithm to fit
-the coefficients. See :ref:`lars_algorithm` for another implementation.
+the coefficients. See :ref:`least_angle_regression` for another implementation.
 
     >>> clf = linear_model.Lasso(alpha = 0.1)
     >>> clf.fit ([[0, 0], [1, 1]], [0, 1])
@@ -200,9 +200,6 @@ The disadvantages of the LARS method include:
 The LARS model can be used using estimator :class:`LARS`, or its
 low-level implementation :func:`lars_path`.
 
-.. topic:: Examples:
-
- * :ref:`example_linear_model_plot_lar.py`
 
 LARS Lasso
 ==========
@@ -351,7 +348,7 @@ Regression* is more robust to ill-posed problem.
 
 .. topic:: References
 
-  * More details can be found in the article `paper <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.27.9072&rep=rep1&type=pdf>`_ 
+  * More details can be found in the article `Bayesian Interpolation <http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.27.9072&rep=rep1&type=pdf>`_ 
     by MacKay, David J. C.
 
 

doc/whats_new.rst

@@ -46,7 +46,7 @@ Changelog
   - New `stochastic gradient
     <http://scikit-learn.sourceforge.net/modules/sgd.html>`_ descent
     module by Peter Prettenhofer. The module comes with complete
-    documentation and some examples can
+    documentation and examples.
 
   - Improved svm module: memory consumption has been reduced by 50%,
     heuristic to automatically set class weights, possibility to
@@ -60,13 +60,13 @@ Changelog
     for a taste of what can be done.
 
   - It is now possible to use liblinear’s Multi-class SVC (option
-    multi_class in :class:`linear_model.LinearSVC`)
+    multi_class in :class:`svm.LinearSVC`)
 
   - New features and performance improvements of text feature
     extraction. 
 
   - Improved sparse matrix support, both in main classes
-    (:class:`grid_search.GridSearch`) as in modules
+    (:class:`grid_search.GridSearchCV`) as in modules
     scikits.learn.svm.sparse and scikits.learn.linear_model.sparse.
 
   - Lots of cool new examples and a new section that uses real-world
@@ -77,8 +77,9 @@ Changelog
     :ref:`example_applications_wikipedia_principal_eigenvector.py` and
     others.
 
-  - Faster LARS algorithm. It is now 2x faster than the R version on
-    worst case and up to 10x times faster on some cases.
+  - Faster :ref:`least_angle_regression` algorithm. It is now 2x
+    faster than the R version on worst case and up to 10x times faster
+    on some cases.
 
   - Faster coordinate descent algorithm. In particular, the full path
     version of lasso (:func:`linear_model.lasso_path`) is more than

examples/applications/README.txt

 
-Exampls based on real world datasets
+Examples based on real world datasets
 ------------------------------------
 
 Applications to real world problems with some medium sized datasets or