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» Randomness and Universal Machines
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68
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COLT
2004
Springer
15 years 2 months ago
Convergence of Discrete MDL for Sequential Prediction
We study the properties of the Minimum Description Length principle for sequence prediction, considering a two-part MDL estimator which is chosen from a countable class of models....
Jan Poland, Marcus Hutter
95
Voted
ALT
1997
Springer
15 years 1 months ago
Learning DFA from Simple Examples
Efficient learning of DFA is a challenging research problem in grammatical inference. It is known that both exact and approximate (in the PAC sense) identifiability of DFA is har...
Rajesh Parekh, Vasant Honavar
82
Voted
COLT
2005
Springer
15 years 3 months ago
Separating Models of Learning from Correlated and Uncorrelated Data
We consider a natural framework of learning from correlated data, in which successive examples used for learning are generated according to a random walk over the space of possibl...
Ariel Elbaz, Homin K. Lee, Rocco A. Servedio, Andr...
FCT
2009
Springer
15 years 4 months ago
Small Weakly Universal Turing Machines
We give small universal Turing machines with state-symbol pairs of (6, 2), (3, 3) and (2, 4). These machines are weakly universal, which means that they have an infinitely repeate...
Turlough Neary, Damien Woods
94
Voted
MCU
2007
144views Hardware» more  MCU 2007»
14 years 11 months ago
Four Small Universal Turing Machines
We present small polynomial time universal Turing machines with state-symbol pairs of (5, 5), (6, 4), (9, 3) and (18, 2). These machines simulate our new variant of tag system, the...
Turlough Neary, Damien Woods