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» Introduction to Randomized Algorithms
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ECML
2006
Springer
15 years 10 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
ICPR
2000
IEEE
15 years 10 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
KDD
2000
ACM
115views Data Mining» more  KDD 2000»
15 years 10 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
171
Voted
CP
1995
Springer
15 years 10 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
SIGGRAPH
1991
ACM
15 years 10 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