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» Approximation Methods for Supervised Learning
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JFR
2006
88views more  JFR 2006»
13 years 8 months ago
Discovering natural kinds of robot sensory experiences in unstructured environments
We derive categories directly from robot sensor data to address the symbol grounding problem. Unlike model-based approaches where human intuitive correspondences are sought betwee...
Daniel H. Grollman, Odest Chadwicke Jenkins, Frank...
JAIR
1998
198views more  JAIR 1998»
13 years 8 months ago
Probabilistic Inference from Arbitrary Uncertainty using Mixtures of Factorized Generalized Gaussians
This paper presents a general and efficient framework for probabilistic inference and learning from arbitrary uncertain information. It exploits the calculation properties of fini...
Alberto Ruiz, Pedro E. López-de-Teruel, M. ...
IUI
2012
ACM
12 years 4 months ago
Probabilistic pointing target prediction via inverse optimal control
Numerous interaction techniques have been developed that make “virtual” pointing at targets in graphical user interfaces easier than analogous physical pointing tasks by invok...
Brian D. Ziebart, Anind K. Dey, J. Andrew Bagnell
CVPR
2012
IEEE
11 years 11 months ago
Complex loss optimization via dual decomposition
We describe a novel max-margin parameter learning approach for structured prediction problems under certain non-decomposable performance measures. Structured prediction is a commo...
Mani Ranjbar, Arash Vahdat, Greg Mori
ALT
2008
Springer
14 years 5 months ago
Computational Models of Neural Representations in the Human Brain
Abstract For many centuries scientists have wondered how the human brain represents thoughts in terms of the underlying biology of neural activity. Philosophers, linguists, cogniti...
Tom M. Mitchell