# HG changeset patch # User Robert McIntyre # Date 1362511083 0 # Node ID 8d08646eaf99fa5e615d952906e66b177eea4276 # Parent 7239aee7267f9beefafc826f83945a7387a5380a remove useless file. diff -r 7239aee7267f -r 8d08646eaf99 MIT-media-projects.org --- a/MIT-media-projects.org Tue Mar 05 18:55:21 2013 +0000 +++ /dev/null Thu Jan 01 00:00:00 1970 +0000 @@ -1,24 +0,0 @@ -*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. - -http://affect.media.mit.edu/projectpages/multimodal/ -