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» Adaptive Kernel Methods Using the Balancing Principle
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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...
PG
2000
IEEE
13 years 12 months ago
A New Adaptive Density Estimator for Particle-Tracing Radiosity
Inparticle-tracing radiosityalgorithms, energy-carrying particles are traced through an environmentfor simulating global illumination. Illumination on a surface is reconstructed f...
Wong Kam Wah
CVPR
2006
IEEE
14 years 9 months ago
Graph Laplacian Kernels for Object Classification from a Single Example
Classification with only one labeled example per class is a challenging problem in machine learning and pattern recognition. While there have been some attempts to address this pr...
Hong Chang, Dit-Yan Yeung
JMLR
2006
116views more  JMLR 2006»
13 years 7 months ago
Step Size Adaptation in Reproducing Kernel Hilbert Space
This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SMD) algorithm to adapt its step size automatically. We formulate the online lear...
S. V. N. Vishwanathan, Nicol N. Schraudolph, Alex ...
JMLR
2012
11 years 10 months ago
Multi Kernel Learning with Online-Batch Optimization
In recent years there has been a lot of interest in designing principled classification algorithms over multiple cues, based on the intuitive notion that using more features shou...
Francesco Orabona, Jie Luo, Barbara Caputo