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ZhenQiu Liu

dblp:06/937 · DBLP profile ↗
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1ranked-venue papers
0as first author
0since 2021 · last 2004
—ORCID · unresolved

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

Artificial intelligence and machine learning · 1Databases, 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
1 paper
Probabilistic and Bayesian machine learning · 67% Kernel, tree and ensemble methods · 33%

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 inference › bayesian inference
bayesian model selection		 0.012004
A Bayesian Framework for Regularized SVM Parameter Estimation · ICDM 2004
Machine learning › Probabilistic and Bayesian machine learning › statistical inference › parameter estimation
hyperparameter estimation		 0.012004
A Bayesian Framework for Regularized SVM Parameter Estimation · ICDM 2004
Machine learning › Kernel, tree and ensemble methods
support vector machine		 0.012004
A Bayesian Framework for Regularized SVM Parameter Estimation · ICDM 2004

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

convex optimization · 0.0bayesian inference · 0.0
YearPublicationVenuePosition
2004 A Bayesian Framework for Regularized SVM Parameter Estimation
abstract
The support vector machine (SVM) is considered here in the context of pattern classification. The emphasis is on the soft margin classifier which uses regularization to handle non-separable learning samples. We present an SVM parameter estimation algorithm that first identifies a subset of the learning samples that we call the support set and then determines not only the weights of the classifier but also the hyperparameter that controls the influence of the regularizing penalty term on basis thereof. We provide numerical results using several data sets from the public domain.
Jens Gregor, ZhenQiu Liu
 ICDM2