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» Solving iterated functions using genetic programming
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140
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ICASSP
2010
IEEE
15 years 3 months ago
A convex relaxation for approximate maximum-likelihood 2D source localization from range measurements
This paper addresses the problem of locating a single source from noisy range measurements in wireless sensor networks. An approximate solution to the maximum likelihood location ...
Pinar Oguz-Ekim, João Pedro Gomes, Jo&atild...
135
Voted
NIPS
1993
15 years 5 months ago
Using Local Trajectory Optimizers to Speed Up Global Optimization in Dynamic Programming
Dynamic programming provides a methodology to develop planners and controllers for nonlinear systems. However, general dynamic programming is computationally intractable. We have ...
Christopher G. Atkeson
141
Voted
EUROGP
2005
Springer
156views Optimization» more  EUROGP 2005»
15 years 9 months ago
Evolving Rules for Document Classification
We describe a novel method for using Genetic Programming to create compact classification rules based on combinations of N-Grams (character strings). Genetic programs acquire fitne...
Laurence Hirsch, Masoud Saeedi, Robin Hirsch
123
Voted
CVPR
2010
IEEE
15 years 11 months ago
Efficiently Selecting Regions for Scene Understanding
Recent advances in scene understanding and related tasks have highlighted the importance of using regions to reason about high-level scene structure. Typically, the regions are ...
M. Pawan Kumar, Daphne Koller
144
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UAI
2004
15 years 5 months ago
Solving Factored MDPs with Continuous and Discrete Variables
Although many real-world stochastic planning problems are more naturally formulated by hybrid models with both discrete and continuous variables, current state-of-the-art methods ...
Carlos Guestrin, Milos Hauskrecht, Branislav Kveto...