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» Computing LTS Regression for Large Data Sets
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KDD
2000
ACM
149views Data Mining» more  KDD 2000»
13 years 11 months ago
Efficient clustering of high-dimensional data sets with application to reference matching
Many important problems involve clustering large datasets. Although naive implementations of clustering are computationally expensive, there are established efficient techniques f...
Andrew McCallum, Kamal Nigam, Lyle H. Ungar
VISSYM
2004
13 years 9 months ago
TimeHistograms for Large, Time-Dependent Data
Histograms are a very useful tool for data analysis, because they show the distribution of values over a data dimension. Many data sets in engineering (like computational fluid dy...
Robert Kosara, Fabian Bendix, Helwig Hauser
NIPS
2001
13 years 9 months ago
Online Learning with Kernels
Abstract--Kernel-based algorithms such as support vector machines have achieved considerable success in various problems in batch setting, where all of the training data is availab...
Jyrki Kivinen, Alex J. Smola, Robert C. Williamson
ICML
2008
IEEE
14 years 8 months ago
Sparse multiscale gaussian process regression
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their computations on a set of m basis functions that are the covariance function of th...
Bernhard Schölkopf, Christian Walder, Kwang I...
COCOA
2008
Springer
13 years 9 months ago
Computational Study on Dominating Set Problem of Planar Graphs
Abstract: Recently, there have been significant theoretical progresses towards fixed-parameter algorithms for the DOMINATING SET problem of planar graphs. It is known that the prob...
Marjan Marzban, Qian-Ping Gu, Xiaohua Jia