TR#553: Context-sensitive Bayesian Classifiers and Application to Mouse Pressure Pattern Classification
Yuan Qi and Rosalind W. Picard
Appears in: International Conference on Pattern Recognition,
Quebec City, Canada, August, 2002.
In this paper, we propose a new context-sensitive Bayesian learning
algorithm. By modeling the distributions of data
locations by a mixture of Gaussians,
the new algorithm can utilize different classifier
complexities for different contexts/locations and, at the same time,
keep the optimality of Bayesian solutions.
This algorithm is also
an online learning algorithm, efficient in training, and easy for
incorporating new knowledge from data sets available in the future.
We apply this algorithm to detecting computer-user mouse pressure patterns
during episodes likely to be frustrating to the user.
By modeling user identity as hidden context, this algorithm achieves
on average $10.6\%$ user-independent test error rate.
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