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ICRA
2003
IEEE
222views Robotics» more  ICRA 2003»
14 years 23 days ago
Path planning using learned constraints and preferences
— In this paper we present a novel method for robot path planning based on learning motion patterns. A motion pattern is defined as the path that results from applying a set of ...
Gregory Dudek, Saul Simhon
GECCO
2008
Springer
147views Optimization» more  GECCO 2008»
13 years 8 months ago
On selecting the best individual in noisy environments
In evolutionary algorithms, the typical post-processing phase involves selection of the best-of-run individual, which becomes the final outcome of the evolutionary run. Trivial f...
Wojciech Jaskowski, Wojciech Kotlowski
SIGMETRICS
2008
ACM
115views Hardware» more  SIGMETRICS 2008»
13 years 7 months ago
Densification arising from sampling fixed graphs
During the past decade, a number of different studies have identified several peculiar properties of networks that arise from a diverse universe, ranging from social to computer n...
Pedram Pedarsani, Daniel R. Figueiredo, Matthias G...

Book
778views
15 years 5 months ago
Gaussian Processes for Machine Learning
"Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning...
Carl Edward Rasmussen and Christopher K. I. Willia...
PAMI
2010
205views more  PAMI 2010»
13 years 5 months ago
Learning a Hierarchical Deformable Template for Rapid Deformable Object Parsing
In this paper, we address the tasks of detecting, segmenting, parsing, and matching deformable objects. We use a novel probabilistic object model that we call a hierarchical defor...
Long Zhu, Yuanhao Chen, Alan L. Yuille