Steven W. Norton

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9ranked-venue papers

6as first author

0since 2021 · last 1996

—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 9 · 6 first-authorGraphics, computer vision, multimedia, augmented reality and games · 3 · 3 first-authorDatabases, data management, data science and information retrieval · 1

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Artificial intelligence 5 papers		Probabilistic and Bayesian machine learning · 62% Trustworthy machine learning · 22% Kernel, tree and ensemble methods · 11%
Interdisciplinary, comprehensive, and emerging computing 3 papers		Bioinformatics and computational biology · 57% Computational finance and economics · 23% Computational social science and digital humanities · 20%
Theoretical computer science 1 paper		Computational complexity · 100%
Databases, data mining, and information retrieval 1 paper		Data mining · 100%

Topics — the 10 heaviest of 12, each with the papers that count most for it

Topic	Papers	Last	Evidence papers
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models bayesian network	1	1996	Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management · ICML 1996
Data mining › probabilistic graphical models bayesian network	1	1995	Knowledge Discovery in Telecommunication Services Data Using Bayesian Network Models · KDD 1995
Bioinformatics and computational biology › gene regulation › promoter analysis promoter prediction	1	1994	Learning to Recognize Promoter Sequences in E. coli by Modeling Uncertainty in the Training Data · AAAI 1994
Computational complexity › learning theory › boolean function learning DNF learning	1	1993	Learning DNF Via Probabilistic Evidence Combination · ICML 1993
Computational complexity learning theory	1	1993	Learning DNF Via Probabilistic Evidence Combination · ICML 1993
Machine learning › Trustworthy machine learning learning from noisy data	1	1992	Classifier Learning from Noisy Data as Probabilistic Evidence Combination · AAAI 1992
Machine learning › Kernel, tree and ensemble methods decision tree learning	1	1989	Generating Better Decision Trees · IJCAI 1989
Computational finance and economics risk management	1	1996	Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management · ICML 1996
Machine learning › Trustworthy machine learning uncertainty modeling	1	1994	Learning to Recognize Promoter Sequences in E. coli by Modeling Uncertainty in the Training Data · AAAI 1994
Machine learning › Learning paradigms › supervised learning classifier training	1	1992	Classifier Learning from Noisy Data as Probabilistic Evidence Combination · AAAI 1992

Methods — techniques the papers use, named apart from their topics

bayesian network · 0.1probabilistic evidence combination · 0.0uncertainty modeling · 0.0classifier learning · 0.0decision tree generation · 0.0

Year	Publication	Venue	Position
1996	Learning Goal Oriented Bayesian Networks for Telecommunications Risk Management Kazuo J. Ezawa, Moninder Singh, Steven W. Norton	ICML	3
1995	Knowledge Discovery in Telecommunication Services Data Using Bayesian Network Models Kazuo J. Ezawa, Steven W. Norton	KDD	2
1994	Learning to Recognize Promoter Sequences in E. coli by Modeling Uncertainty in the Training Data Steven W. Norton	AAAI	1
1993	Learning DNF Via Probabilistic Evidence Combination Steven W. Norton, Haym Hirsh	ICML	1
1992	Classifier Learning from Noisy Data as Probabilistic Evidence Combination Steven W. Norton, Haym Hirsh	AAAI	1
1989	Generating Better Decision Trees Steven W. Norton	IJCAI	1
1986	Selecting expert system frameworks within the Bayesian theory abstract Because of the impact that the choice of an expert system framework has on the success of expert system projects, it is important for anyone evaluating expert systems and expert system tools, whether casually or critically, to consider a variety of issues. As background, this paper provides a general description of the expert systems paradigm, and detailed descriptions of two expert system frameworks for reasoning under uncertainty. Although both of the expert systems described have roots in the Bayesian theory, their differences are significant enough to fuel a discussion of the most significant issues in evaluating the applicability of particular expert system frameworks. We cover knowledge representation and knowledge structures, the initial phases of knowledge engineering, tuning and validating the knowledge base, as well as utilization of the finished expert system. Steven W. Norton	ISMIS	1
1986	Knowledge engineering within a generalised bayesian framework Stephen W. Barth, Steven W. Norton	UAI	2
1986	An explanation mechanism for bayesian inferencing systems Steven W. Norton	UAI	1