Probabilistic reasoning in expert systems - theory and algorithms

By Richard E. Neapolitan. 2012

873 citations recorded by the source corpus.

View Paper: https://www.semanticscholar.org/paper/dc8b13639c55b4d66693ff3ba3d7dad6548d8216

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Learning Bayesian Belief Network Classifiers: Algorithms and System (2001, methodology): “By adding decision nodes and utility nodes, BN models can also be extended to decision networks for decision analysis (Neapolitan, 1990).”
Visual Recognition with Humans in the Loop (2010, background): “Expert systems [12, 13] involve construction of a knowledge base and inference rules that can help non-experts solve a problem.”
Comparison of Machine Learning Classifiers for Influenza Detection from Emergency Department Free-text Reports (2015, methodology): “To build the classifiers we used WEKA’s (Waikato Environment for Knowledge Acquisition, version 3.6 [29]) and compare them to an expert-built Bayesian network (BN) [30] influenza model.”
Feature deduction and ensemble design of intrusion detection systems (2005, background): “Although some of these algorithms can give good results on some benchmark data sets, there are still several problems such as node ordering requirement, lack of efficiency and lack of publicly available learning tools (Neapolitan, 1990).”
Algorithms for Large Scale Markov Blanket Discovery (2003, background): “Bayesian Networks (BN) (Neapolitan 1990) are mathematical objects that compactly represent a joint probability distribution J using a graph G annotated with conditional probabilities; J and G are connected by the Markov Condition property: a node is conditionally independent of its non-descendants,…”
Bayesian Artificial Intelligence (2004, background): “Richard Neapolitan’s Probabilistic Reasoning in Expert Systems [199] complements Pearl’s book nicely, and it lays out the algorithms underlying the technology particularly well.”
Local Causal and Markov Blanket Induction for Causal Discovery and Feature Selection for Classification Part I: Algorithms and Empirical Evaluation (2010, methodology): “Then we call the triplet 〈V ,G,J〉 a Bayesian network (abbreviated as “BN”), or equivalently a belief network or probabilistic network (Neapolitan, 1990).”
User Models for Adaptive Hypermedia and Adaptive Educational Systems (2007, unknown): “As aforementioned, each node must represent a propositional variable [140], which is defined to be as a variable that takes an exhaustive and mutually exclusive number of values.”