Bayesian network models are widely used for discriminative prediction tasks such as classification. Usually their parameters are determined using 'unsupervised' methods ...
A linear matrix inequality (LMI) is a condition stating that a symmetric matrix whose entries are affine linear combinations of variables is positive semidefinite. Motivated by th...
We present an extension of the development of an alternating minimization (AM) method1 for the computation of a specimen's complex transmittance function (magnitude and phase...
In this paper, we describe a novel localization algorithm for ad hoc wireless sensor networks. Accurate selforganization and localization capability is a highly desirable character...
A multitask learning framework is developed for discriminative classification and regression where multiple large-margin linear classifiers are estimated for different predictio...