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CVPR
2000
IEEE
14 years 9 months ago
A General Method for Errors-in-Variables Problems in Computer Vision
The Errors-in-Variables (EIV) model from statistics is often employed in computer vision thoughonlyrarely under this name. In an EIV model all the measurements are corrupted by no...
Bogdan Matei, Peter Meer
TSMC
2008
116views more  TSMC 2008»
13 years 7 months ago
Fuzzy Techniques for Subjective Workload-Score Modeling Under Uncertainties
This paper deals with the development of a computer model to estimate the subjective workload score of individuals by evaluating their heart-rate (HR) signals. The identification o...
Mohit Kumar, D. Arndt, Steffi Kreuzfeld, Kerstin T...
NECO
2007
150views more  NECO 2007»
13 years 7 months ago
Reinforcement Learning, Spike-Time-Dependent Plasticity, and the BCM Rule
Learning agents, whether natural or artificial, must update their internal parameters in order to improve their behavior over time. In reinforcement learning, this plasticity is ...
Dorit Baras, Ron Meir
ISCA
2010
IEEE
232views Hardware» more  ISCA 2010»
13 years 6 months ago
Evolution of thread-level parallelism in desktop applications
As the effective limits of frequency and instruction level parallelism have been reached, the strategy of microprocessor vendors has changed to increase the number of processing ...
Geoffrey Blake, Ronald G. Dreslinski, Trevor N. Mu...
FOCS
1990
IEEE
13 years 11 months ago
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
Avrim Blum