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» Algorithmic Complexity Bounds on Future Prediction Errors
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ICRA
2006
IEEE
87views Robotics» more  ICRA 2006»
15 years 5 months ago
Learning to Predict Slip for Ground Robots
— In this paper we predict the amount of slip an exploration rover would experience using stereo imagery by learning from previous examples of traversing similar terrain. To do t...
Anelia Angelova, Larry Matthies, Daniel M. Helmick...
STACS
2005
Springer
15 years 5 months ago
Sampling Sub-problems of Heterogeneous Max-cut Problems and Approximation Algorithms
Abstract Abstract. Recent work in the analysis of randomized approximation algorithms for NP-hard optimization problems has involved approximating the solution to a problem by the ...
Petros Drineas, Ravi Kannan, Michael W. Mahoney
CORR
2007
Springer
103views Education» more  CORR 2007»
14 years 11 months ago
Physical limits of inference
We show that physical devices that perform observation, prediction, or recollection share an underlying mathematical structure. We call devices with that structure “inference de...
David H. Wolpert
JMLR
2010
112views more  JMLR 2010»
14 years 6 months ago
Reduced-Rank Hidden Markov Models
Hsu et al. (2009) recently proposed an efficient, accurate spectral learning algorithm for Hidden Markov Models (HMMs). In this paper we relax their assumptions and prove a tighte...
Sajid M. Siddiqi, Byron Boots, Geoffrey J. Gordon
COLT
2000
Springer
15 years 4 months ago
PAC Analogues of Perceptron and Winnow via Boosting the Margin
We describe a novel family of PAC model algorithms for learning linear threshold functions. The new algorithms work by boosting a simple weak learner and exhibit complexity bounds...
Rocco A. Servedio