# HG changeset patch
# User atton
# Date 1435128515 -32400
# Node ID 3325edf9139fb9073894514db4a58abf8e94196a
# Parent  8fd9385f952bc6161029b0a2fccfaceff8b2960f
Mini fixes

diff -r 8fd9385f952b -r 3325edf9139f slide.md
--- a/slide.md	Wed Jun 24 15:21:19 2015 +0900
+++ b/slide.md	Wed Jun 24 15:48:35 2015 +0900
@@ -1,4 +1,4 @@
-title: A Novel Greeting System Selection System for a Culture-Adaptive Humanoid Robot
+title: A Novel Greeting Selection System for a Culture-Adaptive Humanoid Robot
 author: Tatsuki KANAGAWA <br> Yasutaka HIGA
 profile: Concurrency Reliance Lab
 lang: Japanese
@@ -117,7 +117,7 @@
 <img src="pictures/model_overview.png" style='width: 75%; margin-left: 120px;'>
 
 # Greeting selection system training data
-* Mappings can be trained to  an initial state with data taken from the literature of sociology studies.
+* Mappings can be trained to an initial state with data taken from the literature of sociology studies.
 * Training data should be classified through some machine learning method or formula.
 * We decided to use conditional probabilities: in particular the Naive Bayes formula to map data.
 * Naive Bayes only requires a small amount of training data.
@@ -137,7 +137,7 @@
 * The mapping is represented by a dataset, initially built from training data, as a table containing weights for each context vector corresponding to each greeting type.
 * We now need to update these weights.
 
-# feedback from three questionnaires
+# feedback from three questionnaires    <!-- FIXME : redundancy -->
 * Whenever a new feature vector is given as an input, it is checked to see whether it is already contained in the dataset or not.
 * In the former case, the weights are directly read from the dataset
 * in the latter case, they get assigned the values of probabilities calculated through the Naive Bayes classifier.