CAJ | 학술논문

支持向量回归(support vector regression,简称SVR)训练算法需要解决在大规模样本条件下的凸二次规划(quadratic programming,简称QP)问题.尽管此种优化算法的机理已经有了较为明确的认识,但已有的支持向量回归训练算法仍较为复杂且收敛速度较慢.为解决这些问题.首先采用扩展方法使SVR与支撑向量机分类(SVC)具有相似的数学形式,并在此基础上针对大规模样本回归问题提出一种用于SVR的简化SOR(successive overrelaxation)算法.实验表明,这种新的回归训练方法在数据量较大时,相对其他训练方法有较快的收敛速度,特别适于在大规模样本条件下的回归训练算法设计.
지지향량회귀(support vector regression,간칭SVR)훈련산법수요해결재대규모양본조건하적철이차규화(quadratic programming,간칭QP)문제.진관차충우화산법적궤리이경유료교위명학적인식,단이유적지지향량회귀훈련산법잉교위복잡차수렴속도교만.위해결저사문제.수선채용확전방법사SVR여지탱향량궤분류(SVC)구유상사적수학형식,병재차기출상침대대규모양본회귀문제제출일충용우SVR적간화SOR(successive overrelaxation)산법.실험표명,저충신적회귀훈련방법재수거량교대시,상대기타훈련방법유교쾌적수렴속도,특별괄우재대규모양본조건하적회귀훈련산법설계.
Training a SVR(support vector regression)requires the solution of a very large QP(quadratic programming)optimization problem.Despite the fact that this type of problem is well understood,the existing training algorithms are very complex and slow.In order to solve these problems,this paper firstly introduces a new way to make SVR have the similar mathematic form as that of a support vector machine.Then a versatile iterative method,successive overrelaxation,is proposed.Experimental results show that this new method converges considerably faster than other methods that require the presence of a substantial amount of data in memory.The results give guidelines for the application of this method to large domains.