Solving Optimization Problem In subject area: Engineering The main aim of solving optimization problems is to find the optimal solution or a set of optimal solutions such that the objective function can be mini
首先是SVM理论本身,包括寻找最大间隔分类超平面、引入核方法极大提高对非线性问题的处理能力、引入松弛变量的软间隔优化,用间隔定量的描述置信风险等等;其次是核方法理论的发展,它独立于SVM本身,这也同时是SVM的一大优点;最后是最优化理论的发展,它同样与SVM具有相对独立性;套个流行话,三者是相辅相成、...
In subject area: Mathematics A CO problem can be modeled by a set of variables to find a satisfying solution respecting a set of constraints while optimizing an objective function. From: Comprehensive Metaheuristics, 2023 About this pageSet alert Also in subject area: Computer ScienceDiscover other...
The paper considers the procedures for solving the multiclass classification problem using a series of support vector machine optimization problems for the binary classification problem in the multicriteria formulation. In the traditional formulation, the objective function takes into account the width of ...
uncertainty and to resolve the problem of parameters optimization in kernel function of support vector machine (SVM), particle swarm optimization (PSO) method, which was originated form artificial life and evolutionary computation, is applied to SVM's parameters selection and optimization in the paper...
According to the characters of swarm intelligence and constrained optimization,this paper proposed a method to solve a linearly constrained quadratic optimization problem in training support vector machines with QPSO.Testified QPSO has determinate applied value in the field of support vector machines,and ...
为解决SVM的对偶问题,John Platt提出了SMO(顺序最小优化)算法。为了引出SMO算法,让我们先讨论coordinate ascent algorithm(坐标上升算法)。 Coordinate ascent 我们已经学过两种优化算法,gradient ascent和Newton' method。我们现在学习的新算法叫做coordinate ascent。考虑一个无约束优化问题: ...
Secondly, the dynamic inertial weight parameters are given to improve the global search speed in the early iterative phase. Thirdly, a new local optimal jump-out strategy is proposed to overcome the "premature" problem. Finally, the algorithm applies the spiral shrinkage search strategy from the ...
[67] proposed a hybridization strategy of combining NM and PSO methods in a way to accelerate convergence. For an n dimensional problem, the procedure proposed by the authors uses 3 n + 1 particles, the particles are sorted by fitness, and the best n particles are saved for subsequent us...
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming (QP) optimization problem. SMO breaks this QP problem into a series of smallest possible QP problems. These small QP problems are solved analytically, which avoids using a time-con...