# HG changeset patch
# User bshanks@bshanks-salk.dyndns.org
# Date 1239503708 25200
# Node ID 7487ad7f5d8f7e18b10984d0536f046f571fa459
# Parent  29eee29f9bc1b34182a6c0ebe5d596ff9b5e5684
.

--- a/.hgignore	Sat Apr 11 19:12:32 2009 -0700
+++ b/.hgignore	Sat Apr 11 19:35:08 2009 -0700
@@ -14,6 +14,7 @@
 *.aux
 *.xref
 *.lg
+*.4??
 
 syntax: regexp
 .*\#.*\#$
--- a/grant.html	Sat Apr 11 19:12:32 2009 -0700
+++ b/grant.html	Sat Apr 11 19:35:08 2009 -0700
@@ -108,12 +108,12 @@
             to label more than a few genes.  Therefore, we must select only a few genes as
             features.
              Principle 3: Use geometry in feature selection
-            When doing feature selection with score-based methods, the simplest thing to
-            do would be to score the performance of each voxel by itself and then combine
-            these scores; this is pointwise scoring. A more powerful approach is to also use
-            information about the geometric relations between each voxel and its neighbors;
-            this requires non-pointwise, local scoring methods. See Preliminary Results for
-            evidence of the complementary nature of pointwise and local scoring methods.
+            When doing feature selection with score-based methods, the simplest thing to do
+            would be to score the performance of each voxel by itself and then combine these
+            scores (pointwise scoring). A more powerful approach is to also use information
+            about the geometric relations between each voxel and its neighbors; this requires
+            non-pointwise, local scoring methods.  See Preliminary Results for evidence of
+            the complementary nature of pointwise and local scoring methods.
              Principle 4: Work in 2-D whenever possible
             There are many anatomical structures which are commonly characterized in
             terms of a two-dimensional manifold. When it is known that the structure that
@@ -121,6 +121,8 @@
             the analysis algorithm to take advantage of this prior knowledge.  In addition,
             it is easier for humans to visualize and work with 2-D data.
                Therefore, when possible, the instances should represent pixels, not voxels.
+             Aim 2
+            todo
                                             3
 
              Aim 3
@@ -173,6 +175,7 @@
             conceivable that the methods we propose to develop could be used to suggest
             modifications to the human cortical map as well.
              Related work
+            todo
              Preliminary work
              Justification of principles 1 thur 3
              Principle 1: Combinatorial gene expression
@@ -198,8 +201,6 @@
             useful.
                The requirement to find combinations of only a small number of genes limits
             us from straightforwardly applying many of the most simple techniques from
-            the field of supervised machine learning.  In the parlance of machine learning,
-            our task combines feature selection with supervised learning.
 __________________________
    1“WW, C2 and coiled-coil domain containing 1”; EntrezGene ID 211652
     2“mitochondrial translational initiation factor 2”; EntrezGene ID 76784
@@ -228,6 +229,8 @@
             genes which (individually) best match area AUD, according to gradient similar-
             ity. From left to right and top to bottom, the genes are Ssr1, Efcbp1, Aph1a,
             Ptk7, Aph1a again, and Lepr
+            the field of supervised machine learning.  In the parlance of machine learning,
+            our task combines feature selection with supervised learning.
              Principle 3: Use geometry
             To show that local geometry can provide useful information that cannot be
             detected via pointwise analyses, consider Fig.  .  The top row of Fig.   displays
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