As sensor networks increase in size and number, efficient techniques are required to process the very large data sets that they generate. Frequently, sensor networks monitor object...
We consider a fundamental problem in computational learning theory: learning an arbitrary Boolean function which depends on an unknown set of k out of n Boolean variables. We give...
Elchanan Mossel, Ryan O'Donnell, Rocco A. Servedio
We developed a machine learning system for determining gene functions from heterogeneous sources of data sets using a Weighted Naive Bayesian Network (WNB). The knowledge of gene ...
Abstract. With the availability of hundreds and soon-to-be thousands of complete genomes, the construction of genome-scale metabolic models for these organisms has attracted much a...
We consider the problem of image classification when more than one visual feature is available. In such cases, Bayes fusion offers an attractive solution by combining the result...