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TIP
2010
155views more  TIP 2010»
14 years 8 months ago
Laplacian Regularized D-Optimal Design for Active Learning and Its Application to Image Retrieval
—In increasingly many cases of interest in computer vision and pattern recognition, one is often confronted with the situation where data size is very large. Usually, the labels ...
Xiaofei He
PERCOM
2004
ACM
15 years 9 months ago
Learning to Detect User Activity and Availability from a Variety of Sensor Data
Using a networked infrastructure of easily available sensors and context-processing components, we are developing applications for the support of workplace interactions. Notions o...
Dave Snowdon, Jean-Luc Meunier, Martin Mühlen...
AIED
2009
Springer
15 years 4 months ago
Fostering engaged and directed learning by activity foregrounding and backgrounding
We propose a design model for guiding learning in exploratory environments through representational choices. Selecting the appropriate representations at the correct granularity ca...
Katherine Howland, Benedict du Boulay, Judith Good
CVIU
2004
132views more  CVIU 2004»
14 years 9 months ago
Layered representations for learning and inferring office activity from multiple sensory channels
We present the use of layered probabilistic representations for modeling human activities, and describe how we use the representation to do sensing, learning, and inference at mul...
Nuria Oliver, Ashutosh Garg, Eric Horvitz
IBPRIA
2009
Springer
15 years 2 months ago
Inference and Learning for Active Sensing, Experimental Design and Control
In this paper we argue that maximum expected utility is a suitable framework for modeling a broad range of decision problems arising in pattern recognition and related fields. Exa...
Hendrik Kück, Matthew Hoffman, Arnaud Doucet,...