A tutorial on hidden Markov models and selected applications in speech recognition
Readings in speech recognition
Robust Classification for Imprecise Environments
Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Editorial: special issue on learning from imbalanced data sets
ACM SIGKDD Explorations Newsletter - Special issue on learning from imbalanced datasets
Distinguishing Text from Graphics in On-Line Handwritten Ink
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Diagram Structure Recognition by Bayesian Conditional Random Fields
CVPR '05 Proceedings of the 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) - Volume 2 - Volume 02
The class imbalance problem: A systematic study
Intelligent Data Analysis
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Complicated by temporal correlations among the strokes and varying distributions of the underlying classes, the drawing/writing classification of ink strokes in a digital ink file poses interesting challenges. In this paper, we present our efforts in addressing some of the issues. First, we describe how we adjust the outputs of the neural network to a priori probabilities of new observations to produce more accurate estimates of the posterior probabilities. Second, we describe how to adapt the parameters of the HMM to new data sets. Albeit the fact that the emission probabilities of the HMM are computed indirectly from the outputs of the neural network, our modified Baum-Welch algorithm still finds the correct estimates for the HMM's parameters. We also present experimental results of our new algorithms on 6 real-world data sets. The results show that our methods increase the F-Measures of both the drawing and the writing classes on the more "drawing-intensive" data sets which have stronger temporal correlations. But they do not perform well on the more "writing-intensive" data sets.