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ICML
1998
IEEE
14 years 8 months ago
Q2: Memory-Based Active Learning for Optimizing Noisy Continuous Functions
This paper introduces a new algorithm, Q2, foroptimizingthe expected output ofamultiinput noisy continuous function. Q2 is designed to need only a few experiments, it avoids stron...
Andrew W. Moore, Jeff G. Schneider, Justin A. Boya...
NN
2010
Springer
187views Neural Networks» more  NN 2010»
13 years 2 months ago
Efficient exploration through active learning for value function approximation in reinforcement learning
Appropriately designing sampling policies is highly important for obtaining better control policies in reinforcement learning. In this paper, we first show that the least-squares ...
Takayuki Akiyama, Hirotaka Hachiya, Masashi Sugiya...
NN
2008
Springer
13 years 7 months ago
Two k-winners-take-all networks with discontinuous activation functions
This paper presents two k-winners-take-all (k-WTA) networks with discontinuous activation functions. The k-WTA operation is first converted equivalently into linear and quadratic ...
Qingshan Liu, Jun Wang
FGR
2006
IEEE
157views Biometrics» more  FGR 2006»
14 years 1 months ago
Evaluating Error Functions for Robust Active Appearance Models
Active appearance models (AAMs) are generative parametric models commonly used to track faces in video sequences. A limitation of AAMs is they are not robust to occlusion. A recen...
Barry-John Theobald, Iain Matthews, Simon Baker
JBCB
2010
138views more  JBCB 2010»
13 years 2 months ago
Hierarchical Classification of Gene Ontology Terms Using the Gostruct Method
Protein function prediction is an active area of research in bioinformatics. And yet, transfer of annotation on the basis of sequence or structural similarity remains widely used ...
Artem Sokolov, Asa Ben-Hur