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GENERALIZED VECTOR QUASIEQUILIBRIUM PROBLEMS WITH SETVALUED MAPPINGS
, 2006
"... A new mathematical model of generalized vector quasiequilibrium problem with setvalued mappings is introduced, and several existence results of a solution for the generalized vector quasiequilibrium problem with and without Φcondensing mapping are shown. The results in this paper extend and unify ..."
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A new mathematical model of generalized vector quasiequilibrium problem with setvalued mappings is introduced, and several existence results of a solution for the generalized vector quasiequilibrium problem with and without Φcondensing mapping are shown. The results in this paper extend
ON EXISTENCE OF A SOLUTION FOR THE SYSTEM OF GENERALIZED VECTOR QUASIEQUILIBRIUM PROBLEMS WITH UPPER SEMICONTINUOUS SETVALUED MAPS
, 2005
"... We introduce a new model of the system of generalized vector quasiequilibrium problems with upper semicontinuous setvalued maps and present several existence results of a solution for this system of generalized vector quasiequilibrium problems and its special cases. The results in this paper exte ..."
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Cited by 3 (2 self)
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We introduce a new model of the system of generalized vector quasiequilibrium problems with upper semicontinuous setvalued maps and present several existence results of a solution for this system of generalized vector quasiequilibrium problems and its special cases. The results in this paper
Corrections and enhancements of quasiequilibrium states
 J. NONNEWTONIAN FLUID MECH. 96 (2001) 203–219
, 2001
"... We give a compact nontechnical presentation of two basic principles for reducing the description of nonequilibrium systems based on the quasiequilibrium approximation. These two principles are: construction of invariant manifolds for the dissipative microscopic dynamics, and coarsegraining for th ..."
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Cited by 27 (17 self)
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graining for the entropyconserving microscopic dynamics. Two new results are presented: first, an application of the invariance principle to hybridization of micro–macro integration schemes is introduced, and is illustrated with nonlinear dumbbell models; second, Ehrenfest’s coarsegraining is extended to general quasiequilibrium
SupportVector Networks
 Machine Learning
, 1995
"... The supportvector network is a new learning machine for twogroup classification problems. The machine conceptually implements the following idea: input vectors are nonlinearly mapped to a very highdimension feature space. In this feature space a linear decision surface is constructed. Special pr ..."
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Cited by 3621 (35 self)
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The supportvector network is a new learning machine for twogroup classification problems. The machine conceptually implements the following idea: input vectors are nonlinearly mapped to a very highdimension feature space. In this feature space a linear decision surface is constructed. Special
Sparse Bayesian Learning and the Relevance Vector Machine
, 2001
"... This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance vec ..."
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Cited by 958 (5 self)
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This paper introduces a general Bayesian framework for obtaining sparse solutions to regression and classication tasks utilising models linear in the parameters. Although this framework is fully general, we illustrate our approach with a particular specialisation that we denote the `relevance
A practical guide to support vector classification
, 2010
"... The support vector machine (SVM) is a popular classification technique. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we propose a simple procedure which usually gives reasonable results. ..."
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Cited by 787 (7 self)
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The support vector machine (SVM) is a popular classification technique. However, beginners who are not familiar with SVM often get unsatisfactory results since they miss some easy but significant steps. In this guide, we propose a simple procedure which usually gives reasonable results.
LIBSVM: a Library for Support Vector Machines
, 2001
"... LIBSVM is a library for support vector machines (SVM). Its goal is to help users can easily use SVM as a tool. In this document, we present all its implementation details. 1 ..."
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Cited by 6287 (82 self)
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LIBSVM is a library for support vector machines (SVM). Its goal is to help users can easily use SVM as a tool. In this document, we present all its implementation details. 1
Training Support Vector Machines: an Application to Face Detection
, 1997
"... We investigate the application of Support Vector Machines (SVMs) in computer vision. SVM is a learning technique developed by V. Vapnik and his team (AT&T Bell Labs.) that can be seen as a new method for training polynomial, neural network, or Radial Basis Functions classifiers. The decision sur ..."
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Cited by 728 (1 self)
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global optimality, and can be used to train SVM's over very large data sets. The main idea behind the decomposition is the iterative solution of subproblems and the evaluation of optimality conditions which are used both to generate improved iterative values, and also establish the stopping
Transductive Inference for Text Classification using Support Vector Machines
, 1999
"... This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try to minimiz ..."
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Cited by 887 (4 self)
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This paper introduces Transductive Support Vector Machines (TSVMs) for text classification. While regular Support Vector Machines (SVMs) try to induce a general decision function for a learning task, Transductive Support Vector Machines take into account a particular test set and try
Results 1  10
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2,089,802