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Zoë Hoare

dblp:75/1572 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2008
0000-0003-1803-5482ORCID · corroborated

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

Artificial intelligence and machine learning · 2 · 1 first-author

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
1 paper
Probabilistic and Bayesian machine learning · 87% Learning theory · 13%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning › statistical dependence
feature dependency		 0.112008
Error-Dependency Relationships for the Naïve Bayes Classifier with Binary Features · IEEE Trans. Pattern Anal. Mach. Intell. 2008
Machine learning › Probabilistic and Bayesian machine learning › structured models › graphical models › bayesian network › bayesian network classifiers
naive bayes		 0.112008
Error-Dependency Relationships for the Naïve Bayes Classifier with Binary Features · IEEE Trans. Pattern Anal. Mach. Intell. 2008
Machine learning › Learning theory › classification
classification error analysis		 0.012008
Error-Dependency Relationships for the Naïve Bayes Classifier with Binary Features · IEEE Trans. Pattern Anal. Mach. Intell. 2008

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

dependency measure · 0.1bayesian error analysis · 0.1
YearPublicationVenuePosition
2008 Landscapes of Naïve Bayes classifiers
Zoë Hoare
 Pattern Anal. Appl.1
2008 Error-Dependency Relationships for the Naïve Bayes Classifier with Binary Features
abstract
We derive a tight dependency-related bound on the difference between the Naïve Bayes (NB) error and Bayes error for two binary features and two equiprobable classes. A measure of discrepancy of feature dependencies is proposed for multiple features. Its correlation with NB is shown using 23 real data sets.
Ludmila I. Kuncheva, Zoë Hoare
 IEEE Trans. Pattern Anal. Mach. Intell.2