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CVPR
2005
IEEE
14 years 7 months ago
WaldBoost - Learning for Time Constrained Sequential Detection
: In many computer vision classification problems, both the error and time characterizes the quality of a decision. We show that such problems can be formalized in the framework of...
Jan Sochman, Jiri Matas
ICCBR
2009
Springer
13 years 11 months ago
Case-Based Collective Inference for Maritime Object Classification
Maritime assets such as merchant and navy ships, ports, and harbors, are targets of terrorist attacks as evidenced by the USS Cole bombing. Conventional methods of securing maritim...
Kalyan Moy Gupta, David W. Aha, Philip Moore
KDD
2006
ACM
174views Data Mining» more  KDD 2006»
14 years 5 months ago
Onboard classifiers for science event detection on a remote sensing spacecraft
Typically, data collected by a spacecraft is downlinked to Earth and pre-processed before any analysis is performed. We have developed classifiers that can be used onboard a space...
Ashley Davies, Benjamin Cichy, Dominic Mazzoni, Ng...
ML
2010
ACM
135views Machine Learning» more  ML 2010»
13 years 1 days ago
Multi-domain learning by confidence-weighted parameter combination
State-of-the-art statistical NLP systems for a variety of tasks learn from labeled training data that is often domain specific. However, there may be multiple domains or sources o...
Mark Dredze, Alex Kulesza, Koby Crammer
ECCV
2010
Springer
13 years 5 months ago
MIForests: Multiple-Instance Learning with Randomized Trees
Abstract. Multiple-instance learning (MIL) allows for training classifiers from ambiguously labeled data. In computer vision, this learning paradigm has been recently used in many ...
Christian Leistner, Amir Saffari, Horst Bischof