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Jesper Vedelsby

dblp:70/1450 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 1994
—ORCID · none

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

Artificial intelligence and machine learning · 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
1 paper
Kernel, tree and ensemble methods · 33% Learning theory · 33% Efficient and distributed learning · 33%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Efficient and distributed learning
active learning		 0.011994
Neural Network Ensembles, Cross Validation, and Active Learning · NIPS 1994
Machine learning › Learning theory › model selection
cross-validation		 0.011994
Neural Network Ensembles, Cross Validation, and Active Learning · NIPS 1994
Machine learning › Kernel, tree and ensemble methods
ensemble learning		 0.011994
Neural Network Ensembles, Cross Validation, and Active Learning · NIPS 1994
Machine learning › Learning theory › generalization error
generalization error estimation		 0.011994
Neural Network Ensembles, Cross Validation, and Active Learning · NIPS 1994
Machine learning › Kernel, tree and ensemble methods › ensemble learning
neural network ensemble		 0.011994
Neural Network Ensembles, Cross Validation, and Active Learning · NIPS 1994
Machine learning › Efficient and distributed learning › active learning › disagreement-based active learning
query by committee		 0.011994
Neural Network Ensembles, Cross Validation, and Active Learning · NIPS 1994

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

query-by-committee · 0.0ensemble methods · 0.0cross-validation · 0.0
YearPublicationVenuePosition
1994 Neural Network Ensembles, Cross Validation, and Active Learning
abstract
Learning of continuous valued functions using neural network en(cid:173) sembles (committees) can give improved accuracy, reliable estima(cid:173) tion of the generalization error, and active learning. The ambiguity is defined as the variation of the output of ensemble members aver(cid:173) aged over unlabeled data, so it quantifies the disagreement among the networks. It is discussed how to use the ambiguity in combina(cid:173) tion with cross-validation to give a reliable estimate of the ensemble generalization error, and how this type of ensemble cross-validation can sometimes improve performance. It is shown how to estimate the optimal weights of the ensemble members using unlabeled data. By a generalization of query by committee, it is finally shown how the ambiguity can be used to select new training data to be labeled in an active learning scheme.
Anders Krogh, Jesper Vedelsby
 NIPS2