JMLR
2012
12 years 1 months ago
2012
While determining model complexity is an important problem in machine learning, many feature learning algorithms rely on cross-validation to choose an optimal number of features, ...
UAI
1990
14 years 18 days ago
1990
The intelligent reformulation or restructuring of a belief network can greatly increase the efficiency of inference. However, time expended for reformulation is not available for ...
UAI
1996
14 years 23 days ago
1996
Developing a large belief network, like any large system, requires systems engineering to manage the design and construction process. We propose that network engineering follow a ...
UAI
1996
14 years 23 days ago
1996
Inference algorithms for arbitrary belief networks are impractical for large, complex belief networks. Inference algorithms for specialized classes of belief networks have been sh...
UAI
1996
14 years 23 days ago
1996
Recent research has found that diagnostic performance with Bayesian belief networks is often surprisingly insensitive to imprecision in the numerical probabilities. For example, t...
FLAIRS
1998
14 years 24 days ago
1998
The increasing number of knowledge-based systems that build on a Bayesian belief network or influence diagram acknowledge the usefulness of these frameworks for addressing complex...
UAI
2003
14 years 24 days ago
2003
In non-ergodic belief networks the posterior belief of many queries given evidence may become zero. The paper shows that when belief propagation is applied iteratively over arbitr...
UAI
2004
14 years 24 days ago
2004
The paper introduces mixed networks, a new framework for expressing and reasoning with probabilistic and deterministic information. The framework combines belief networks with con...
MMNS
2001
14 years 25 days ago
2001
Abstract. In this paper, we present a framework for supporting intelligent fault and performance management for communication networks. Belief networks are taken as the basis for k...
CL
2000
Springer
14 years 3 months ago
2000
Springer
In this paper I give a brief overview of recent work on uncertainty inAI, and relate it to logical representations. Bayesian decision theory and logic are both normative frameworks...