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CVPR
2012
IEEE
13 years 9 months ago
Batch mode Adaptive Multiple Instance Learning for computer vision tasks
Multiple Instance Learning (MIL) has been widely exploited in many computer vision tasks, such as image retrieval, object tracking and so on. To handle ambiguity of instance label...
Wen Li, Lixin Duan, Ivor Wai-Hung Tsang, Dong Xu
193
Voted
ICCV
2007
IEEE
16 years 8 months ago
Learning 3-D Scene Structure from a Single Still Image
We consider the problem of estimating detailed 3-d structure from a single still image of an unstructured environment. Our goal is to create 3-d models which are both quantitative...
Ashutosh Saxena, Min Sun, Andrew Y. Ng
ECAL
2005
Springer
16 years 7 days ago
The Quantitative Law of Effect is a Robust Emergent Property of an Evolutionary Algorithm for Reinforcement Learning
An evolutionary reinforcement-learning algorithm, the operation of which was not associated with an optimality condition, was instantiated in an artificial organism. The algorithm ...
J. J. McDowell, Zahra Ansari
ECAL
1995
Springer
15 years 10 months ago
Can Development Be Designed? What we May Learn from the Cog Project
Neither `design' nor `evolutionary' approaches to building behavior-based robots feature a role for development in the genesis of behavioral organization. However, the n...
Julie C. Rutkowska
BMCBI
2007
91views more  BMCBI 2007»
15 years 6 months ago
A machine learning approach for the identification of odorant binding proteins from sequence-derived properties
Background: Odorant binding proteins (OBPs) are believed to shuttle odorants from the environment to the underlying odorant receptors, for which they could potentially serve as od...
Ganesan Pugalenthi, E. Ke Tang, Ponnuthurai N. Sug...