Comparing paper and tangible, multimodal tools
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Using redundant speech and handwriting for learning new vocabulary and understanding abbreviations
Proceedings of the 8th international conference on Multimodal interfaces
Information Fusion in Multimedia Information Retrieval
Adaptive Multimedial Retrieval: Retrieval, User, and Semantics
Can feature information interaction help for information fusion in multimedia problems?
Multimedia Tools and Applications
Multimodal Interfaces: A Survey of Principles, Models and Frameworks
Human Machine Interaction
Mudra: a unified multimodal interaction framework
ICMI '11 Proceedings of the 13th international conference on multimodal interfaces
Fusion in multimodal interactive systems: an HMM-based algorithm for user-induced adaptation
Proceedings of the 4th ACM SIGCHI symposium on Engineering interactive computing systems
Error-correcting output codes based ensemble feature extraction
Pattern Recognition
A dialogue system for multimodal human-robot interaction
Proceedings of the 15th ACM on International conference on multimodal interaction
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When building a complex pattern recognizer with high-dimensional input features, a number of selection uncertainties arise. Traditional approaches to resolving these uncertainties typically rely either on the researcher's intuition or performance evaluation on validation data, both of which result in poor generalization and robustness on test data. This paper describes a novel recognition technique called members to teams to committee (MTC), which is designed to reduce modeling uncertainty. In particular, the MTC posterior estimator is based on a coordinated set of divide-and-conquer estimators that derive from a three-tiered architectural structure corresponding to individual members, teams, and the overall committee. Basically, the MTC recognition decision is determined by the whole empirical posterior distribution, rather than a single estimate. This paper describes the application of the MTC technique to handwritten gesture recognition and multimodal system integration and presents a comprehensive analysis of the characteristics and advantages of the MTC approach.