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» Support Vector Classification with Input Data Uncertainty
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MCS
2005
Springer
14 years 1 months ago
Ensemble of SVMs for Incremental Learning
Support Vector Machines (SVMs) have been successfully applied to solve a large number of classification and regression problems. However, SVMs suffer from the catastrophic forgetti...
Zeki Erdem, Robi Polikar, Fikret S. Gürgen, N...
IJDMB
2007
110views more  IJDMB 2007»
13 years 7 months ago
Transductive learning with EM algorithm to classify proteins based on phylogenetic profiles
: Phylogenetic profiles of proteins  strings of ones and zeros encoding respectively the presence and absence of proteins in a group of genomes  have recently been used to id...
Roger A. Craig, Li Liao
ICML
2006
IEEE
14 years 8 months ago
Nonstationary kernel combination
The power and popularity of kernel methods stem in part from their ability to handle diverse forms of structured inputs, including vectors, graphs and strings. Recently, several m...
Darrin P. Lewis, Tony Jebara, William Stafford Nob...
ANOR
2006
133views more  ANOR 2006»
13 years 7 months ago
Horizon and stages in applications of stochastic programming in finance
To solve a decision problem under uncertainty via stochastic programming means to choose or to build a suitable stochastic programming model taking into account the nature of the r...
Marida Bertocchi, Vittorio Moriggia, Jitka Dupacov...
VLSISP
2010
254views more  VLSISP 2010»
13 years 6 months ago
Manifold Based Local Classifiers: Linear and Nonlinear Approaches
Abstract In case of insufficient data samples in highdimensional classification problems, sparse scatters of samples tend to have many ‘holes’—regions that have few or no nea...
Hakan Cevikalp, Diane Larlus, Marian Neamtu, Bill ...