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» Sampling Techniques for Kernel Methods
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KDD
2002
ACM
138views Data Mining» more  KDD 2002»
16 years 3 months ago
Learning to match and cluster large high-dimensional data sets for data integration
Part of the process of data integration is determining which sets of identifiers refer to the same real-world entities. In integrating databases found on the Web or obtained by us...
William W. Cohen, Jacob Richman
113
Voted
SIBGRAPI
2005
IEEE
15 years 8 months ago
Analytic Antialiasing for Selective High Fidelity Rendering
Images rendered using global illumination algorithms are considered amongst the most realistic in 3D computer graphics. However, this high fidelity comes at a significant comput...
Peter Longhurst, Kurt Debattista, Richard Gillibra...
ETT
2002
77views Education» more  ETT 2002»
15 years 2 months ago
On the importance function in splitting simulation
The splitting method is a simulation technique for the estimation of very small probabilities. In this technique, the sample paths are split into multiple copies, at various stages...
Marnix J. J. Garvels, Jan-Kees C. W. van Ommeren, ...
CVPR
2008
IEEE
16 years 5 months ago
Semi-Supervised Discriminant Analysis using robust path-based similarity
Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality r...
Yu Zhang, Dit-Yan Yeung
ICML
2007
IEEE
16 years 3 months ago
Transductive regression piloted by inter-manifold relations
In this paper, we present a novel semisupervised regression algorithm working on multiclass data that may lie on multiple manifolds. Unlike conventional manifold regression algori...
Huan Wang, Shuicheng Yan, Thomas S. Huang, Jianzhu...