Demonstration venue · read-only. Every page can be browsed; the buttons that would change it are switched off. Create an account to run TaxoReview on your own data. A simulation from public publication records: the people listed were not invited and did not agree to review.
? Tutorial for this page
Demo Data from EDBT 2026 / reviewers in the wild / expert

Yoon K. Kim

dblp:45/106 · DBLP profile ↗
← Back
1ranked-venue papers
0as first author
0since 2021 · last 1999
—ORCID · none

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

Artificial intelligence and machine learning · 1Graphics, computer vision, multimedia, augmented reality and games · 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
Image recognition and object detection · 50% Probabilistic and Bayesian machine learning · 50%
Databases, data mining, and information retrieval
1 paper
Data mining · 100%

Topics — the 4 heaviest of 4, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Machine learning › Probabilistic and Bayesian machine learning
fuzzy classification		 0.011999
A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values · CVPR 1999
Computer vision › Image recognition and object detection
image classification		 0.011999
A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values · CVPR 1999
Data mining
clustering		 0.011999
A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values · CVPR 1999
Data mining › predictive modeling › classification
nearest neighbor classification		 0.011999
A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values · CVPR 1999

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

variance weighting · 0.0membership values · 0.0
YearPublicationVenuePosition
1999 A Fuzzy K-NN Algorithm Using Weights from the Variance of Membership Values
abstract
In this paper, a new fuzzy K-nearest neighbor (K-NN) algorithm, called "Variance Weighted Fuzzy K-NN", is proposed. The main idea of this method is in giving weights to neighbors according to the standard deviation of their class membership values which reflect the value of a discriminant function. The classification results of 32 classes of complex images are given. Compared to the K-NN and fuzzy K-NN algorithms, our method shows an improved classification rate for various conditions.
Joon H. Han, Yoon K. Kim
 CVPR2