Classical and weighted knowledgebase transformations
نویسندگان
چکیده
منابع مشابه
compactifications and function spaces on weighted semigruops
chapter one is devoted to a moderate discussion on preliminaries, according to our requirements. chapter two which is based on our work in (24) is devoted introducting weighted semigroups (s, w), and studying some famous function spaces on them, especially the relations between go (s, w) and other function speces are invesigated. in fact this chapter is a complement to (32). one of the main fea...
15 صفحه اولTransformations between Signed and Classical Clause Logic
In the last years two automated reasoning techniques for clause normal form arose in which the use of labels are prominently featured: signed logic and annotated logic programming, which can be embedded into the first. The underlying basic idea is to generalise the classical notion of a literal by adorning an atomic formula with a sign or label which in general consists of a possibly ordered se...
متن کاملDeduction of Lorentz Transformations from Classical Thermodynamics
The Lorentz transformations are obtained by assuming that the laws of classical thermodynamics are invariant under changes of inertial reference frames. As Maxwell equations are used in order to deduce a wave equation that shows the constancy of the speed of light, by means of the laws of classical thermodynamics, the invariance of the Carnot cycle is deduced under reference frame changes. Star...
متن کاملPerceptually weighted linear transformations for voice conversion
Voice conversion is a technique for modifying a source speaker’s speech to sound as if it was spoken by a target speaker. A popular approach to voice conversion is to apply a linear transformation to the spectral envelope. However, conventional parameter estimation based on least square error optimization does not necessarily lead to the best perceptual result. In this paper, a perceptually wei...
متن کاملWeighted Model Integration with Orthogonal Transformations
Weighted model counting and integration (WMC/WMI) are natural problems to which we can reduce many probabilistic inference tasks, e.g., in Bayesian networks, Markov networks, and probabilistic programs. Typically, we are given a first-order formula, where each satisfying assignment is associated with a weight—e.g., a probability of occurrence—and our goal is to compute the total weight of the f...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Computers & Mathematics with Applications
سال: 1996
ISSN: 0898-1221
DOI: 10.1016/0898-1221(96)00137-x