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KDD
2006
ACM
165views Data Mining» more  KDD 2006»
16 years 7 months ago
Training linear SVMs in linear time
Linear Support Vector Machines (SVMs) have become one of the most prominent machine learning techniques for highdimensional sparse data commonly encountered in applications like t...
Thorsten Joachims
KDD
2002
ACM
130views Data Mining» more  KDD 2002»
16 years 7 months ago
Learning domain-independent string transformation weights for high accuracy object identification
The task of object identification occurs when integrating information from multiple websites. The same data objects can exist in inconsistent text formats across sites, making it ...
Sheila Tejada, Craig A. Knoblock, Steven Minton
ICDM
2009
IEEE
155views Data Mining» more  ICDM 2009»
16 years 1 months ago
Stacked Gaussian Process Learning
—Triggered by a market relevant application that involves making joint predictions of pedestrian and public transit flows in urban areas, we address the question of how to utili...
Marion Neumann, Kristian Kersting, Zhao Xu, Daniel...
PKDD
2009
Springer
103views Data Mining» more  PKDD 2009»
16 years 1 months ago
Kernels for Periodic Time Series Arising in Astronomy
Abstract. We present a method for applying machine learning algorithms to the automatic classification of astronomy star surveys using time series of star brightness. Currently su...
Gabriel Wachman, Roni Khardon, Pavlos Protopapas, ...
ICDM
2006
IEEE
155views Data Mining» more  ICDM 2006»
16 years 20 days ago
Automatic Single-Organ Segmentation in Computed Tomography Images
In this paper, we propose a hybrid approach for automatic single-organ segmentation in Computed Tomography (CT) data. The approach consists of three stages: first, a probability i...
Ruchaneewan Susomboon, Daniela Stan Raicu, Jacob D...