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...
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 ...
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...
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...
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 ...