[Gzz-commits] gzz/Documentation/Manuscripts/Paper paper.tex

[Tuomas J. Lukka]

CVSROOT:        /cvsroot/gzz
Module name:    gzz
Changes by:     Tuomas J. Lukka <address@hidden>        02/11/19 15:35:13

Modified files:
        Documentation/Manuscripts/Paper: paper.tex 

Log message:
        movestuff

CVSWeb URLs:
http://savannah.gnu.org/cgi-bin/viewcvs/gzz/gzz/Documentation/Manuscripts/Paper/paper.tex.diff?tr1=1.39&tr2=1.40&r1=text&r2=text

Patches:
Index: gzz/Documentation/Manuscripts/Paper/paper.tex
diff -u gzz/Documentation/Manuscripts/Paper/paper.tex:1.39 
gzz/Documentation/Manuscripts/Paper/paper.tex:1.40
--- gzz/Documentation/Manuscripts/Paper/paper.tex:1.39  Tue Nov 19 15:19:39 2002
+++ gzz/Documentation/Manuscripts/Paper/paper.tex       Tue Nov 19 15:35:10 2002
@@ -205,6 +205,25 @@
 if all circles were green and all squares yellow, a considerable amount of
 bits would be wasted.
 
+To understand why it is possible to learn to discriminate particular
+textures easily, consider the task of learning {\em one} texture.
+This is a two-class problem.
+Extensive literature...
+Here,
+there are two categories: the paper A and everything else.
+This problem is quite easy to solve: the probability of error
+decreases exponentially in the number of features.
+
+
+A perceptron\cite{XXX} is a simple neural network with XXX.
+In the input layer, various features are activated, 
+... linear combination of features.
+
+Easy to learn when many features, STRICT CORRELATION
+
+$m$ binary features, $N$ vectors. Probability that
+
+
 Easiest to remember presence and absence of features; therefore, should have
 relatively small basis size, not to have too many features. XXX why?
 
@@ -685,30 +704,6 @@
 4 textures, texture shading. These correspond to G400,  GeForce2 and
 GeForce3.
 
-\section{Experiment}
-
-\section{A neurocomputing interpretation}
-
-The recognizability of the generated textures is perhaps surprising
-in the light of the experiments on XXX..
-
-To understand why it is possible to learn to discriminate particular
-textures easily, consider the task of learning {\em one} texture.
-This is a two-class problem.
-Extensive literature...
-Here,
-there are two categories: the paper A and everything else.
-This problem is quite easy to solve: the probability of error
-decreases exponentially in the number of features.
-
-
-A perceptron\cite{XXX} is a simple neural network with XXX.
-In the input layer, various features are activated, 
-... linear combination of features.
-
-Easy to learn when many features, STRICT CORRELATION
-
-$m$ binary features, $N$ vectors. Probability that
 
 
 \section{Conclusions}