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» Discovering Classification from Data of Multiple Sources
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114
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IDA
2007
Springer
15 years 1 months ago
Removing biases in unsupervised learning of sequential patterns
Unsupervised sequence learning is important to many applications. A learner is presented with unlabeled sequential data, and must discover sequential patterns that characterize th...
Yoav Horman, Gal A. Kaminka
CHI
2004
ACM
16 years 2 months ago
Variation in element and action: supporting simultaneous development of alternative solutions
The complexity of many problems necessitates creating and exploring multiple, alternative solutions. However, current user interfaces do not cleanly support creating alternatives ...
Michael A. Terry, Elizabeth D. Mynatt, Kumiyo Naka...
IVC
2010
121views more  IVC 2010»
15 years 4 days ago
Multiview segmentation and tracking of dynamic occluding layers
We present an algorithm for the layered segmentation of video data in multiple views. The approach is based on computing the parameters of a layered representation of the scene in...
Ian D. Reid, Keith Connor
106
Voted
CVPR
2008
IEEE
16 years 3 months ago
Mining compositional features for boosting
The selection of weak classifiers is critical to the success of boosting techniques. Poor weak classifiers do not perform better than random guess, thus cannot help decrease the t...
Junsong Yuan, Jiebo Luo, Ying Wu
115
Voted
IPCCC
2007
IEEE
15 years 8 months ago
Streaming in MANET: Proactive Link Protection and Receiver-Oriented Adaptation
Multimedia streaming applications can significantly boost the value of mobile ad hoc networks (MANET). Live streaming, however, means continuous data delivery, which is a major c...
Toby Xu, Ymg Cai