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» Entropy Numbers of Linear Function Classes
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DCC
2008
IEEE
14 years 7 months ago
High-Resolution Functional Quantization
Suppose a function of N real source variables XN 1 = (X1, X2, . . . , XN ) is desired at a destination constrained to receive a limited number of bits. If the result of evaluating...
Vinith Misra, Vivek K. Goyal, Lav R. Varshney
ISMVL
2000
IEEE
120views Hardware» more  ISMVL 2000»
13 years 12 months ago
Mod-p Decision Diagrams: A Data Structure for Multiple-Valued Functions
Multiple-valued decision diagrams (MDDs) give a way of approaching problems by using symbolic variables which are often more naturally associated with the problem statement than t...
Harald Sack, Elena Dubrova, Christoph Meinel
DATAMINE
1999
143views more  DATAMINE 1999»
13 years 7 months ago
Partitioning Nominal Attributes in Decision Trees
To find the optimal branching of a nominal attribute at a node in an L-ary decision tree, one is often forced to search over all possible L-ary partitions for the one that yields t...
Don Coppersmith, Se June Hong, Jonathan R. M. Hosk...
ICML
1995
IEEE
14 years 8 months ago
Residual Algorithms: Reinforcement Learning with Function Approximation
A number of reinforcement learning algorithms have been developed that are guaranteed to converge to the optimal solution when used with lookup tables. It is shown, however, that ...
Leemon C. Baird III
EUROCOLT
1999
Springer
13 years 12 months ago
Averaging Expert Predictions
We consider algorithms for combining advice from a set of experts. In each trial, the algorithm receives the predictions of the experts and produces its own prediction. A loss func...
Jyrki Kivinen, Manfred K. Warmuth