By James R. Wilcox
This specific factor contains many of the awesome paintings initially provided on the ACM Multimedia convention 2003 (ACM MM 2003). The convention acquired 255 submissions, of which forty three fine quality papers have been permitted for presentation. of those papers, the Technical software Chairs invited a dozen authors to post more desirable types in their papers to this targeted factor. those papers went via a rigorous evaluation strategy, and we're chuffed to provide 4 actually impressive papers during this specialissue. as a result hugely aggressive assessment method and restricted house, many glorious papers couldn't be authorized for this distinct factor. in spite of the fact that, a few of them are being forwarded for attention as destiny general papers during this magazine.
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Additional resources for ACM Transactions on Multimedia Computing, Communications and Applications (May)
2002]. All these approaches assume that C, P , and L are fixed. The ability to improve C, P , and L is where our proposed CDE approach differs from these static ones. The core of CDE is its ability to assess classification confidence. The concept of classification confidence is not new in the field of pattern recognition. Chow  proposed using the “rejection” option to refuse classifying a low-confidence instance. To measure confidence, various methods [Bouchaffra et al. 1999; Platt 1999] have been proposed to map the output of a binary classifier into a posterior probability value, which represents the confidence in a “yes” prediction.
The trained classifier predicts a label in C to annotate u. However, there is no way to assess whether the prediction is accurate. (3) Inability to Comprehend the Causes of a Misprediction. Suppose a misclassified instance is discovered eventually by a user. A traditional static classifier cannot provide adequate clues to explain why the misprediction has taken place. In this work, we propose a confidence-based dynamic ensemble (CDE) to alleviate the above shortcomings. In contrast to the traditional static ensemble schemes, CDE aims to make intelligent adjustments on C, P , and L.
CULLER, D. , AND PISTER, K. 2000. System architecture directions for networked sensors. In Architectural Support for Programming Languages and Operating Systems. pp. 83–104. , AND FENG, W. 2003. Quality-adaptive media streaming by priority drop. In Proceedings of the 13th International Workshop on Network and Operating Systems Support for Digital Audio and Video (NOSSDAV 2003), June. KRAVETS, R. AND KRISHNAN, P. 2000. Application driven power management for mobile communication. , 6, 4, 263–277.
ACM Transactions on Multimedia Computing, Communications and Applications (May) by James R. Wilcox