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ECML
2004
Springer
13 years 11 months ago
Efficient Hyperkernel Learning Using Second-Order Cone Programming
The kernel function plays a central role in kernel methods. Most existing methods can only adapt the kernel parameters or the kernel matrix based on empirical data. Recently, Ong e...
Ivor W. Tsang, James T. Kwok
ICPR
2006
IEEE
14 years 8 months ago
Adaptive Feature Integration for Segmentation of 3D Data by Unsupervised Density Estimation
In this paper, a novel unsupervised approach for the segmentation of unorganized 3D points sets is proposed. The method derives by the mean shift clustering paradigm devoted to se...
Marco Cristani, Umberto Castellani, Vittorio Murin...
CSDA
2007
101views more  CSDA 2007»
13 years 7 months ago
The evaluation of evidence for exponentially distributed data
At present, likelihood ratios for two-level models are determined with the use of a normal kernel estimation procedure when the between-group distribution is thought to be non-nor...
C. G. G. Aitken, Qiang Shen, Richard Jensen, B. Ha...
CIKM
2006
Springer
13 years 11 months ago
Resource-aware kernel density estimators over streaming data
A fundamental building block of many data mining and analysis approaches is density estimation as it provides a comprehensive statistical model of a data distribution. For that re...
Christoph Heinz, Bernhard Seeger
MICCAI
2006
Springer
14 years 8 months ago
Anatomically Informed Convolution Kernels for the Projection of fMRI Data on the Cortical Surface
Abstract. We present here a method that aims at producing representations of functional brain data on the cortical surface from functional MRI volumes. Such representations are req...
Grégory Operto, Jean-Luc Anton, Olivier Cou...