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» Introduction to Randomized Algorithms
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ECML
2006
Springer
15 years 7 months ago
Active Learning with Irrelevant Examples
Abstract. Active learning algorithms attempt to accelerate the learning process by requesting labels for the most informative items first. In real-world problems, however, there ma...
Dominic Mazzoni, Kiri Wagstaff, Michael C. Burl
130
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ICPR
2000
IEEE
15 years 7 months ago
MRF Solutions for Probabilistic Optical Flow Formulations
In this paper we propose an efficient, non-iterative method for estimating optical flow. We develop a probabilistic framework that is appropriate for describing the inherent uncer...
Sébastien Roy, Venu Govindu
132
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KDD
2000
ACM
115views Data Mining» more  KDD 2000»
15 years 7 months ago
Mining asynchronous periodic patterns in time series data
Periodicy detection in time series data is a challenging problem of great importance in many applications. Most previous work focused on mining synchronous periodic patterns and d...
Jiong Yang, Wei Wang 0010, Philip S. Yu
131
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CP
1995
Springer
15 years 7 months ago
Optimization-based Heuristics for Maximal Constraint Satisfaction
We present a new heuristic approach for maximal constraint satisfaction of overconstrained problems (MAX-CSP). This approach is based on a formulation of CSP as an optimization pro...
Javier Larrosa, Pedro Meseguer
101
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SIGGRAPH
1991
ACM
15 years 7 months ago
Spectrally optimal sampling for distribution ray tracing
Nonuniform sampling of images is a useful technique in computer graphics, because a properly designed pattern of samples can make aliasing take the form of high-frequency random n...
Don P. Mitchell