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» Sampling Techniques for Kernel Methods
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WSC
2007
15 years 4 days ago
Ant-based approach for determining the change of measure in importance sampling
Importance Sampling is a potentially powerful variance reduction technique to speed up simulations where the objective depends on the occurrence of rare events. However, it is cru...
Poul E. Heegaard, Werner Sandmann
CVPR
2010
IEEE
15 years 6 months ago
Locally-Parametric Pictorial Structures
Pictorial structure (PS) models are extensively used for part-based recognition of scenes, people, animals and multi-part objects. To achieve tractability, the structure and param...
Benjamin Sapp, Chris Jordan, Ben Taskar
AAAI
2008
15 years 4 days ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
ICRA
1995
IEEE
188views Robotics» more  ICRA 1995»
15 years 1 months ago
Fast Approximation of Range Images by Triangular Meshes Generated through Adaptive Randomized Sampling
This paper describes and evaluates an efficient technique that allows the fast generation of 3D triangular meshes from range images avoiding optimization procedures. Such a tool ...
Miguel Angel García
ICASSP
2011
IEEE
14 years 1 months ago
Generic object recognition using automatic region extraction and dimensional feature integration utilizing multiple kernel learn
Recently, in generic object recognition research, a classification technique based on integration of image features is garnering much attention. However, with a classifying techn...
Toru Nakashika, Akira Suga, Tetsuya Takiguchi, Yas...