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The naive Bayes classifier greatly simplify learning by assuming that features are independent given class. Although independence is generally a poor assumption, in practice naive Bayes often competes well with more sophisticated classifiers. Our broad goal is to understand the data characteristics which affect the performance of naive Bayes. Our approach(More)
Real-time problem diagnosis in large distributed computer systems and networks is a challenging task that requires fast and accurate inferences from potentially huge data volumes. In this paper, we propose a cost-efficient, adaptive diagnostic technique called active probing . Probes are end-to-end test transactions that collect information about the(More)
We explore to what extent the combination of predictive and interpretable modeling can provide new insights for functional brain imaging. For this, we apply a recently introduced regularized regression technique, the Elastic Net, to the analysis of the PBAIC 2007 competition data. Elastic Net regression controls via one parameter the number of voxels in the(More)
This article presents a class of approximation algorithms that extend the idea of bounded-complexity inference, inspired by successful constraint propagation algorithms, to probabilistic inference and combinatorial optimization. The idea is to bound the dimensionality of dependencies created by inference algorithms. This yields a parameterized scheme,(More)
As the complexity of distributed computing systems increases, systems management tasks require significantly higher levels of automation; examples include diagnosis and prediction based on real-time streams of computer events, setting alarms, and performing continuous monitoring. The core of <i>autonomic computing</i>, a recently proposed initiative towards(More)
We consider the problem of diagnosing faults in a system represented by a Bayesian network, where diagnosis corresponds to recovering the most likely state of unobserved nodes given the outcomes of tests (observed nodes). Finding an optimal subset of tests in this setting is intractable in general. We show that it is difficult even to compute the next(More)
The paper compares two popular strategies for solving propositional satisfiability, backtracking search and resolution, and analyzes the complexity of a directional resolution algorithm (DR) as a function of the “width” (w *) of the problem"s graph. Our empirical evaluation confirms theoretical prediction, showing that on low-w * problems DR is very(More)
The paper describes a class of approximation algorithms that apply the idea of local inference, known as i-consistency in constraint networks, to combinatorial optimization and to probabilistic reasoning. Our approach is based on bucket elimination [13] and o ers adjustable levels of accuracy and e ciency. We identify regions of completeness and demonstrate(More)
One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to interand intra-subject differences, as well as to inherent noise associated with EEG data collection. Herein, we propose a novel approach for learning such representations from multichannel EEG time-series, and demonstrate(More)