In an educational setting, a teacher plays a crucial role in various classroom teaching patterns. Similarly, mirroring this aspect of human learning, the learning using privileged information (LUPI) paradigm introduces additional information to instruct learning models during the training stage. A different approach to train the twin variant of the regression model is provided by the new least square twin support vector regression using privileged information (LSTSVR-PI), which integrates the LUPI paradigm to utilize additional sources of information into the least square twin support vector regression. The proposed LSTSVR-PI solves system of linear equations which adds up to the efficiency of the model. Further, we also establish a generalization error bound based on the Rademacher complexity of the proposed model and incorporate the structural risk minimization principle. The proposed LSTSVR-PI fills the gap between the contemporary paradigm of LUPI and classical LSTSVR. Further, to assess the performance of the proposed model, we conduct numerical experiments along with the baseline models across various artificially generated and real-world datasets. The various experiments and statistical analysis infer the superiority of the proposed model. Moreover, as an application, we conduct experiments on time series datasets, which results in the superiority of the proposed LSTSVR-PI.

翻译：在教育场景中，教师在不同课堂教学模式中起着关键作用。类似地，模仿人类学习的这一特点，基于特权信息的学习（LUPI）范式在训练阶段引入额外信息来指导学习模型。基于特权信息的最小二乘孪生支持向量回归（LSTSVR-PI）提供了一种训练回归模型孪生变体的新方法，该方法融合了LUPI范式，以利用额外信息源来改进最小二乘孪生支持向量回归。所提出的LSTSVR-PI通过求解线性方程组提升模型效率。此外，我们基于所提模型的Rademacher复杂度建立了泛化误差界，并纳入了结构风险最小化原则。所提出的LSTSVR-PI填补了当代LUPI范式与经典LSTSVR之间的空白。进一步地，为评估所提模型的性能，我们在多个人工生成和真实数据集上进行了数值实验，并与基线模型进行对比。各项实验与统计分析均表明所提模型的优越性。此外，作为应用，我们在时间序列数据集上开展实验，结果进一步证实了所提LSTSVR-PI的优越性。