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     1.4 +*Machine Learning and Pattern Recognition with Multiple Modalities
     1.5 +Hyungil Ahn and Rosalind W. Picard
     1.6 +
     1.7 +This project develops new theory and algorithms to enable computers to
     1.8 +make rapid and accurate inferences from multiple modes of data, such
     1.9 +as determining a person's affective state from multiple sensors—video,
    1.10 +mouse behavior, chair pressure patterns, typed selections, or
    1.11 +physiology. Recent efforts focus on understanding the level of a
    1.12 +person's attention, useful for things such as determining when to
    1.13 +interrupt. Our approach is Bayesian: formulating probabilistic models
    1.14 +on the basis of domain knowledge and training data, and then
    1.15 +performing inference according to the rules of probability
    1.16 +theory. This type of sensor fusion work is especially challenging due
    1.17 +to problems of sensor channel drop-out, different kinds of noise in
    1.18 +different channels, dependence between channels, scarce and sometimes
    1.19 +inaccurate labels, and patterns to detect that are inherently
    1.20 +time-varying. We have constructed a variety of new algorithms for
    1.21 +solving these problems and demonstrated their performance gains over
    1.22 +other state-of-the-art methods.
    1.23 +
    1.24 +http://affect.media.mit.edu/projectpages/multimodal/
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