--- a/doc/v2_planning/main_plan.txt	Fri Sep 17 20:24:30 2010 -0400
+++ b/doc/v2_planning/main_plan.txt	Fri Sep 17 20:55:18 2010 -0400
@@ -3,7 +3,7 @@
 ==========
 
 Yoshua (points discussed Thursday Sept 2, 2010 at LISA tea-talk)
-------
+----------------------------------------------------------------
 
 ****** Why we need to get better organized in our code-writing ******
 
@@ -151,6 +151,7 @@
 Another thing to consider related to datasets is that there are a number of
 other efforts to have standard ML datasets, and we should be aware of them,
 and compatible with them when it's easy:
+
  - mldata.org    (they have a file format, not sure how many use it)
  - weka          (ARFF file format)
  - scikits.learn 
@@ -168,10 +169,10 @@
 Yoshua (about ideas proposed by Pascal Vincent a while ago): 
 
   - we may want to distinguish between datasets and tasks: a task defines
-  not just the data but also things like what is the input and what is the
-  target (for supervised learning), and *importantly* a set of performance metrics
-  that make sense for this task (e.g. those used by papers solving a particular
-  task, or reported for a particular benchmark)
+    not just the data but also things like what is the input and what is the
+    target (for supervised learning), and *importantly* a set of performance metrics
+    that make sense for this task (e.g. those used by papers solving a particular
+    task, or reported for a particular benchmark)
 
   - we should discuss about a few "standards" that datasets and tasks may comply to, such as
     - "input" and "target" fields inside each example, for supervised or semi-supervised learning tasks