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» On the Performance of Clustering in Hilbert Spaces
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VLDB
2000
ACM
229views Database» more  VLDB 2000»
15 years 1 months ago
Local Dimensionality Reduction: A New Approach to Indexing High Dimensional Spaces
Many emerging application domains require database systems to support efficient access over highly multidimensional datasets. The current state-of-the-art technique to indexing hi...
Kaushik Chakrabarti, Sharad Mehrotra
CVPR
2009
IEEE
16 years 5 months ago
A Nonparametric Riemannian Framework for Processing High Angular Resolution Diffusion Images (HARDI)
High angular resolution diffusion imaging has become an important magnetic resonance technique for in vivo imaging. Most current research in this field focuses on developing met...
Alvina Goh, Christophe Lenglet, Paul M. Thompson, ...
89
Voted
CVPR
2006
IEEE
16 years 6 days ago
Accelerated Kernel Feature Analysis
A fast algorithm, Accelerated Kernel Feature Analysis (AKFA), that discovers salient features evidenced in a sample of n unclassified patterns, is presented. Like earlier kernel-b...
Xianhua Jiang, Yuichi Motai, Robert R. Snapp, Xing...
83
Voted
ICML
2005
IEEE
15 years 11 months ago
Beyond the point cloud: from transductive to semi-supervised learning
Due to its occurrence in engineering domains and implications for natural learning, the problem of utilizing unlabeled data is attracting increasing attention in machine learning....
Vikas Sindhwani, Partha Niyogi, Mikhail Belkin
NIPS
2004
14 years 11 months ago
Worst-Case Analysis of Selective Sampling for Linear-Threshold Algorithms
We provide a worst-case analysis of selective sampling algorithms for learning linear threshold functions. The algorithms considered in this paper are Perceptron-like algorithms, ...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...