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» On Unbiased Linear Approximations
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ICML
2006
IEEE
15 years 10 months ago
Efficient co-regularised least squares regression
In many applications, unlabelled examples are inexpensive and easy to obtain. Semisupervised approaches try to utilise such examples to reduce the predictive error. In this paper,...
Stefan Wrobel, Thomas Gärtner, Tobias Scheffe...
ICML
2005
IEEE
15 years 10 months ago
Predictive low-rank decomposition for kernel methods
Low-rank matrix decompositions are essential tools in the application of kernel methods to large-scale learning problems. These decompositions have generally been treated as black...
Francis R. Bach, Michael I. Jordan
ICML
2005
IEEE
15 years 10 months ago
Core Vector Regression for very large regression problems
In this paper, we extend the recently proposed Core Vector Machine algorithm to the regression setting by generalizing the underlying minimum enclosing ball problem. The resultant...
Ivor W. Tsang, James T. Kwok, Kimo T. Lai
ICML
2003
IEEE
15 years 10 months ago
TD(0) Converges Provably Faster than the Residual Gradient Algorithm
In Reinforcement Learning (RL) there has been some experimental evidence that the residual gradient algorithm converges slower than the TD(0) algorithm. In this paper, we use the ...
Ralf Schoknecht, Artur Merke
VLSID
2002
IEEE
130views VLSI» more  VLSID 2002»
15 years 10 months ago
Using Randomized Rounding to Satisfy Timing Constraints of Real-Time Preemptive Tasks
In preemptive real-time systems, a tighter estimate of the Worst Case Response Time(WCRT) of the tasks can be obtained if the layout of the tasks in memory is included in the esti...
Anupam Datta, Sidharth Choudhury, Anupam Basu