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» An ASM-Characterization of a Class of Distributed Algorithms
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ICPR
2002
IEEE
15 years 10 months ago
Classification Using a Hierarchical Bayesian Approach
A key problem faced by classifiers is coping with styles not represented in the training set. We present an application of hierarchical Bayesian methods to the problem of recogniz...
Charles Mathis, Thomas M. Breuel
FOCS
1990
IEEE
15 years 1 months ago
Separating Distribution-Free and Mistake-Bound Learning Models over the Boolean Domain
Two of the most commonly used models in computational learning theory are the distribution-free model in which examples are chosen from a fixed but arbitrary distribution, and the ...
Avrim Blum
PODC
2012
ACM
13 years 2 days ago
Weak models of distributed computing, with connections to modal logic
This work presents a classification of weak models of distributed computing. We focus on deterministic distributed algorithms, and we study models of computing that are weaker ve...
Lauri Hella, Matti Järvisalo, Antti Kuusisto,...
IJCAI
2001
14 years 11 months ago
Distributed Monitoring of Hybrid Systems: A model-directed approach
This paper presents an efficient online mode estimation algorithm for a class of sensor-rich, distributed embedded systems, the so-called hybrid systems. A central problem in dist...
Feng Zhao, Xenofon D. Koutsoukos, Horst W. Haussec...
APPROX
2005
Springer
80views Algorithms» more  APPROX 2005»
15 years 3 months ago
On Learning Random DNF Formulas Under the Uniform Distribution
Abstract: We study the average-case learnability of DNF formulas in the model of learning from uniformly distributed random examples. We define a natural model of random monotone ...
Jeffrey C. Jackson, Rocco A. Servedio