Zobrazeno 1 - 10
of 69
pro vyhledávání: '"Goldszmidt, Moises"'
Autor:
Mussmann, Stephen, Reisler, Julia, Tsai, Daniel, Mousavi, Ehsan, O'Brien, Shayne, Goldszmidt, Moises
Active learning has been studied extensively as a method for efficient data collection. Among the many approaches in literature, Expected Error Reduction (EER) (Roy and McCallum) has been shown to be an effective method for active learning: select th
Externí odkaz:
http://arxiv.org/abs/2211.09283
Autor:
Boutilier, Craig, Goldszmidt, Moises
This is the Proceedings of the Sixteenth Conference on Uncertainty in Artificial Intelligence, which was held in San Francisco, CA, June 30 - July 3, 2000
Externí odkaz:
http://arxiv.org/abs/1304.3842
Autor:
Goldszmidt, Moises, Pearl, Judea
We propose a norm of consistency for a mixed set of defeasible and strict sentences, based on a probabilistic semantics. This norm establishes a clear distinction between knowledge bases depicting exceptions and those containing outright contradictio
Externí odkaz:
http://arxiv.org/abs/1304.1507
Autor:
Goldszmidt, Moises, Pearl, Judea
We recently described a formalism for reasoning with if-then rules that re expressed with different levels of firmness [18]. The formalism interprets these rules as extreme conditional probability statements, specifying orders of magnitude of disbeli
Externí odkaz:
http://arxiv.org/abs/1303.5406
Autor:
Darwiche, Adnan, Goldszmidt, Moises
We study the connection between kappa calculus and probabilistic reasoning in diagnosis applications. Specifically, we abstract a probabilistic belief network for diagnosing faults into a kappa network and compare the ordering of faults computed usin
Externí odkaz:
http://arxiv.org/abs/1302.6797
Autor:
Darwiche, Adnan, Goldszmidt, Moises
This work proposes action networks as a semantically well-founded framework for reasoning about actions and change under uncertainty. Action networks add two primitives to probabilistic causal networks: controllable variables and persistent variables
Externí odkaz:
http://arxiv.org/abs/1302.6796
Autor:
Goldszmidt, Moises
We present an algorithm, called Predict, for updating beliefs in causal networks quantified with order-of-magnitude probabilities. The algorithm takes advantage of both the structure and the quantification of the network and presents a polynomial asy
Externí odkaz:
http://arxiv.org/abs/1302.4950
Autor:
Friedman, Nir, Goldszmidt, Moises
In this paper we examine a novel addition to the known methods for learning Bayesian networks from data that improves the quality of the learned networks. Our approach explicitly represents and learns the local structure in the conditional probabilit
Externí odkaz:
http://arxiv.org/abs/1302.3577
Bayesian networks provide a language for qualitatively representing the conditional independence properties of a distribution. This allows a natural and compact representation of the distribution, eases knowledge acquisition, and supports effective i
Externí odkaz:
http://arxiv.org/abs/1302.3562
Autor:
Friedman, Nir, Goldszmidt, Moises
There is an obvious need for improving the performance and accuracy of a Bayesian network as new data is observed. Because of errors in model construction and changes in the dynamics of the domains, we cannot afford to ignore the information in new d
Externí odkaz:
http://arxiv.org/abs/1302.1538