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AAAI
2008
13 years 10 months 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...
MIR
2005
ACM
198views Multimedia» more  MIR 2005»
14 years 1 months ago
Semi-automatic video annotation based on active learning with multiple complementary predictors
In this paper, we will propose a novel semi-automatic annotation scheme for video semantic classification. It is well known that the large gap between high-level semantics and low...
Yan Song, Xian-Sheng Hua, Li-Rong Dai, Meng Wang
CVPR
2011
IEEE
13 years 5 months ago
Dynamic Batch Mode Active Learning
Active learning techniques have gained popularity in reducing human effort to annotate data instances for inducing a classifier. When faced with large quantities of unlabeled dat...
Shayok Chakraborty, Vineeth Balasubramanian, Sethu...
ICRA
2009
IEEE
130views Robotics» more  ICRA 2009»
14 years 2 months ago
Model adaptation with least-squares SVM for adaptive hand prosthetics
— The state-of-the-art in control of hand prosthetics is far from optimal. The main control interface is represented by surface electromyography (EMG): the activation potentials ...
Francesco Orabona, Claudio Castellini, Barbara Cap...