孪生参数化不敏感支持向量回归机(twin parametric insensitive support vector regression,简称TPISVR)是一种新型机器学习方法.与其他回归方法相比,TPISVR在处理异方差噪声方面具有独特的优势.标准TPISVR的训练算法可以归结为在对偶空间求解一对具有不等式约束的二次规划问题.然而,这种求解方法的时间消耗比较大.引入最小二乘思想,将TPISVR的两个二次规划问题转化为两个线性方程组,并在原始空间上直接求解,提出了最小二乘孪生参数化不敏感支持向量回归机(least squares TPISVR,简称LSTPISVR).为了解决LSTPISVR的参数选择问题,提出了混沌布谷鸟优化算法,并用其对LSTPISVR的参数进行优化选择.在人工数据集和UCI数据集上的实验结果表明:LSTPISVR在保持精度不下降的情况下,具有更高的运行效率.%Twin parametric insensitive support vector regression (TPISVR) is a novel machine learning method proposed.Compared to other regression methods,TPISVR has unique advantages in dealing with heteroscedastic noise.Standard TPISVR can be attributed to solve a pair of quadratic programming problem (QPP) with inequality constraints in the dual space.However,this method is subject to the constraints of time and memory when number of samples are large.This paper introduces the least squares ideas,and proposes the least squares twin parametric insensitive support vector regression (LSTPISVR) which transforms the two QPPs of TPISVR into linear equations and solves them directly on the original space.Further,a chaotic cuckoo optimization algorithm is introduced for parameter selection of LSTPISVR.Experiments on artificial datasets and UCI datasets show that LSTPISVR not only has fast learning speed,but also shows good generalization performance.
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