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Lacey Gunter

dblp:82/3371 · DBLP profile ↗
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3ranked-venue papers
3as first author
0since 2021 · last 2007
—ORCID · none

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

Artificial intelligence and machine learning · 2 · 2 first-authorApplied, interdisciplinary, general and emerging computing · 1 · 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
Optimization for machine learning · 60% Kernel, tree and ensemble methods · 20% Learning theory · 20%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Learning theory
model selection		 0.112005
Computing the Solution Path for the Regularized Support Vector Regression · NIPS 2005
Machine learning › Optimization for machine learning › hyperparameter optimization
regularization parameter selection		 0.112005
Computing the Solution Path for the Regularized Support Vector Regression · NIPS 2005
Machine learning › Optimization for machine learning › regularized risk minimization
regularized regression		 0.112005
Computing the Solution Path for the Regularized Support Vector Regression · NIPS 2005
Machine learning › Optimization for machine learning
solution path		 0.112005
Computing the Solution Path for the Regularized Support Vector Regression · NIPS 2005
Machine learning › Kernel, tree and ensemble methods › support vector machine
support vector regression		 0.112005
Computing the Solution Path for the Regularized Support Vector Regression · NIPS 2005

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

solution path algorithm · 0.1degrees of freedom estimation · 0.1
YearPublicationVenuePosition
2007 Variable Selection for Optimal Decision Making
Lacey Gunter, Susan A. Murphy
 AIME1
2007 Efficient Computation and Model Selection for the Support Vector Regression
abstract
In this letter, we derive an algorithm that computes the entire solution path of the support vector regression (SVR). We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.
Lacey Gunter
 Neural Comput.1
2005 Computing the Solution Path for the Regularized Support Vector Regression
abstract
In this paper we derive an algorithm that computes the entire solu- tion path of the support vector regression, with essentially the same computational cost as fitting one SVR model. We also propose an unbiased estimate for the degrees of freedom of the SVR model, which allows convenient selection of the regularization parameter.
Lacey Gunter
 NIPS1