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» A Framework for Implicitly Tracking Data
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SDM
2010
SIAM
144views Data Mining» more  SDM 2010»
13 years 9 months ago
A Probabilistic Framework to Learn from Multiple Annotators with Time-Varying Accuracy
This paper addresses the challenging problem of learning from multiple annotators whose labeling accuracy (reliability) differs and varies over time. We propose a framework based ...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
DILS
2006
Springer
13 years 11 months ago
Link Discovery in Graphs Derived from Biological Databases
Public biological databases contain vast amounts of rich data that can also be used to create and evaluate new biological hypothesis. We propose a method for link discovery in biol...
Petteri Sevon, Lauri Eronen, Petteri Hintsanen, Ki...
TSP
2011
152views more  TSP 2011»
13 years 2 months ago
Road Intensity Based Mapping Using Radar Measurements With a Probability Hypothesis Density Filter
Abstract—Mapping stationary objects is essential for autonomous vehicles and many autonomous functions in vehicles. In this contribution the probability hypothesis density (PHD) ...
Christian Lundquist, Lars Hammarstrand, Fredrik Gu...
ICCV
2009
IEEE
15 years 21 days ago
Learning Pedestrian Dynamics from the Real World
In this paper we describe a method to learn parameters which govern pedestrian motion by observing video data. Our learning framework is based on variational mode learning and a...
Paul Scovanner, Marshall Tappen
ISMB
2000
13 years 9 months ago
Intelligent Aids for Parallel Experiment Planning and Macromolecular Crystallization
This paper presents a framework called Parallel Experiment Planning (PEP) that is based on an abstraction of how experiments are performed in the domain of macromolecular crystall...
Vanathi Gopalakrishnan, Bruce G. Buchanan, John M....