A semi-supervised support vector regression based on Help-Training is proposed, which includes two kinds of
learners: a least squares support vector regression(LS-SVR) and a ??-nearest neighbor(??NN). As a main learner, the LSSVR
chooses unlabeled samples with the highest confidence to label and adds these samples to the labeled sample set,
which is repeated for given iterations to enlarge the scale of the training samples so as to improve the property of function
approximation of the LS-SVR. As an auxiliary learner, the ??NN is used to help the LS-SVR choose unlabeled samples to
evaluate confidence from a high-density region of training samples, which can weaken the negative influence of noise on
the learning performance of the LS-SVR. Experimental results show that the Help-Training LS-SVR has advantages of good
regression performance and high learning accuracy.