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» Sampling Techniques for Kernel Methods
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ICONIP
2004
14 years 11 months ago
Semi-supervised Kernel-Based Fuzzy C-Means
This paper presents a semi-supervised kernel-based fuzzy c-means algorithm called S2KFCM by introducing semi-supervised learning technique and the kernel method simultaneously into...
Daoqiang Zhang, Keren Tan, Songcan Chen
PAKDD
2005
ACM
142views Data Mining» more  PAKDD 2005»
15 years 3 months ago
Dynamic Cluster Formation Using Level Set Methods
Density-based clustering has the advantages for (i) allowing arbitrary shape of cluster and (ii) not requiring the number of clusters as input. However, when clusters touch each o...
Andy M. Yip, Chris H. Q. Ding, Tony F. Chan
COMAD
2008
14 years 11 months ago
Disk-Based Sampling for Outlier Detection in High Dimensional Data
We propose an efficient sampling based outlier detection method for large high-dimensional data. Our method consists of two phases. In the first phase, we combine a "sampling...
Timothy de Vries, Sanjay Chawla, Pei Sun, Gia Vinh...
SIGIR
2008
ACM
14 years 9 months ago
Generalising multiple capture-recapture to non-uniform sample sizes
Algorithms in distributed information retrieval often rely on accurate knowledge of the size of a collection. The "multiple capture-recapture" method of Shokouhi et al. ...
Paul Thomas
CGF
2010
153views more  CGF 2010»
14 years 10 months ago
Localized Delaunay Refinement for Sampling and Meshing
The technique of Delaunay refinement has been recognized as a versatile tool to generate Delaunay meshes of a variety of geometries. Despite its usefulness, it suffers from one la...
Tamal K. Dey, Joshua A. Levine, A. Slatton