LSTSVR-PI: Least square twin support vector regression with privileged information

• URL: http://arxiv.org/abs/2312.02596v2
• Date: Wed, 21 Feb 2024 15:22:07 GMT
• Title: LSTSVR-PI: Least square twin support vector regression with privileged information
• Authors: Anuradha Kumari, M. Tanveer
• Abstract summary: We propose a new least square twin support vector regression using privileged information (LSTSVR-PI) It integrates the LUPI paradigm to utilize additional sources of information into the least square twin support vector regression. The proposed model fills the gap between the contemporary paradigm of LUPI and classical LSTSVR.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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