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KDD
2006
ACM
165views Data Mining» more  KDD 2006»
15 years 10 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
JCP
2008
167views more  JCP 2008»
14 years 9 months ago
Accelerated Kernel CCA plus SVDD: A Three-stage Process for Improving Face Recognition
kernel canonical correlation analysis (KCCA) is a recently addressed supervised machine learning methods, which shows to be a powerful approach of extracting nonlinear features for...
Ming Li, Yuanhong Hao
ICDE
2003
IEEE
146views Database» more  ICDE 2003»
15 years 11 months ago
Similarity Search in Sets and Categorical Data Using the Signature Tree
Data mining applications analyze large collections of set data and high dimensional categorical data. Search on these data types is not restricted to the classic problems of minin...
Nikos Mamoulis, David W. Cheung, Wang Lian
CCE
2004
14 years 9 months ago
Retrospective on optimization
In this paper we provide a general classification of mathematical optimization problems, followed by a matrix of applications that shows the areas in which these problems have bee...
Lorenz T. Biegler, Ignacio E. Grossmann
SIAMSC
2008
142views more  SIAMSC 2008»
14 years 8 months ago
Algebraic Multigrid Solvers for Complex-Valued Matrices
In the mathematical modeling of real-life applications, systems of equations with complex coefficients often arise. While many techniques of numerical linear algebra, e.g., Krylovs...
Scott MacLachlan, Cornelis W. Oosterlee