# HG changeset patch
# User Robert McIntyre <rlm@mit.edu>
# Date 1342801341 18000
# Node ID c264ebf683b4dbdc37b508726b20f2a36df0c4cf
# Parent  f4ef73370da1e9f169814192eb06f0e1a5842ca2
cleanup.

diff -r f4ef73370da1 -r c264ebf683b4 MIT-media-projects.org
--- a/MIT-media-projects.org	Fri Jul 20 11:21:04 2012 -0500
+++ b/MIT-media-projects.org	Fri Jul 20 11:22:21 2012 -0500
@@ -1,21 +1,24 @@
-*Machine Learning and Pattern Recognition with Multiple Modalities
-Hyungil Ahn and Rosalind W. Picard
+*Machine Learning and Pattern Recognition with Multiple
+Modalities Hyungil Ahn and Rosalind W. Picard
 
-This project develops new theory and algorithms to enable computers to
-make rapid and accurate inferences from multiple modes of data, such
-as determining a person's affective state from multiple sensors—video,
-mouse behavior, chair pressure patterns, typed selections, or
-physiology. Recent efforts focus on understanding the level of a
-person's attention, useful for things such as determining when to
-interrupt. Our approach is Bayesian: formulating probabilistic models
-on the basis of domain knowledge and training data, and then
-performing inference according to the rules of probability
-theory. This type of sensor fusion work is especially challenging due
-to problems of sensor channel drop-out, different kinds of noise in
-different channels, dependence between channels, scarce and sometimes
-inaccurate labels, and patterns to detect that are inherently
-time-varying. We have constructed a variety of new algorithms for
-solving these problems and demonstrated their performance gains over
-other state-of-the-art methods.
+This project develops new theory and algorithms to enable
+computers to make rapid and accurate inferences from
+multiple modes of data, such as determining a person's
+affective state from multiple sensors--video, mouse behavior,
+chair pressure patterns, typed selections, or
+physiology. Recent efforts focus on understanding the level
+of a person's attention, useful for things such as
+determining when to interrupt. Our approach is Bayesian:
+formulating probabilistic models on the basis of domain
+knowledge and training data, and then performing inference
+according to the rules of probability theory. This type of
+sensor fusion work is especially challenging due to problems
+of sensor channel drop-out, different kinds of noise in
+different channels, dependence between channels, scarce and
+sometimes inaccurate labels, and patterns to detect that are
+inherently time-varying. We have constructed a variety of
+new algorithms for solving these problems and demonstrated
+their performance gains over other state-of-the-art methods.
 
-http://affect.media.mit.edu/projectpages/multimodal/
\ No newline at end of file
+http://affect.media.mit.edu/projectpages/multimodal/
+