Results 31 to 40 of about 6,719,109 (88)
Robust Linear and Support Vector Regression [PDF]
, 2000 The robust Huber M-estimator, a differentiable cost function that is quadratic for small errors and linear otherwise, is modeled exactly, in the original primal space of the problem, by an easily solvable simple convex quadratic program for both linear ...
Mangasarian, Olvi, Musicant, David
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"Most tantumising state of affairs": Mathematical and non-mathematical in quantum-like understanding of thinking. [PDF]
Front Psychol, 2022 Plotnitsky A.
europepmc +1 more source
Two approaches toward constrained vector optimization and identity of the solutions [PDF]
In this paper we deal with a Fritz John type constrained vector optimization problem. In spite that there are many concepts of solutions for an unconstrained vector optimization problem, we show the possibility “to double” the number of concepts when a ...
Rocca Matteo +2 more
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Symmetry breaking and effects of nutrient walkway in time-dependent bone remodeling incorporating poroelasticity. [PDF]
Biomech Model Mechanobiol, 2022 Esposito L +3 more
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Радиус устойчивости мажоритарно эффективного решения многокритериальной линейной траекторной задачи
, 2002 Formula for stability radius of a majority efficient solution of a linear trajectorial problem has been ...
Степанишина, Юлия Всеволодовна
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Semismooth Support Vector Machines [PDF]
, 2000 The linear support vector machine can be posed as a quadratic pro- gram in a variety of ways. In this paper, we look at a formulation using the two-norm for the misclassi cation error that leads to a positive de - nite quadratic program with a single ...
Ferris, Michael, Munson, Todd
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Binarized support vector machines [PDF]
, 2007 The widely used Support Vector Machine (SVM) method has shown to yield very good results in Supervised Classification problems. Other methods such as Classification Trees have become more popular among practitioners than SVM thanks to their ...
Martin-Barragan, Belen +2 more
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Data Selection for Support Vector Machine Classifiers [PDF]
, 2000 The problem of extracting a minimal number of data points from a large dataset, in order to generate a support vector machine (SVM) classi er, is formulated as a concave minimization problem and solved by a nite number of linear programs.
Olvi L. Mangasarian +3 more
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Multiobjective Lagrangian duality for portfolio optimization with risk measures [PDF]
In this paper we present an application for a multiobjective optimization problem. The objective functions of the primal problem are the risk and the expected pain associated to a portfolio vector. Then, we present a Lagrangian dual problem for it.
Elisa Pagani
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, 1997 We consider a vector discrete optimization problem on a system of non-empty subsets (trajectories) of a finite set. The vector criterion of the problem consists of partial criteria of the kinds MINSUM, MINMAX and MINMIN.
Emilicev, V.A. (Belaruski Dzyarzhawny Univ., Minsk (Belarus)) +3 more
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