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TWSVR: Regression via Twin Support Vector Machine.

Reshma Khemchandani1, Keshav Goyal2, Suresh Chandra2

  • 1Department of Computer Science, Faculty of Mathematics and Computer Science, South Asian University, Delhi, India.

Neural Networks : the Official Journal of the International Neural Network Society
|December 2, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Twin Support Vector Regression (TWSVR) formulation, aligning with the core principles of Twin Support Vector Machines (TWSVM). The proposed TWSVR outperforms existing methods like TSVR and Support Vector Regression (SVR) in regression tasks.

Keywords:
Machine LearningSupport Vector MachinesSupport vector regressionTwin Support Vector Machines

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Area of Science:

  • Machine Learning
  • Computational Statistics

Background:

  • Twin Support Vector Machines (TWSVM) offer a robust framework for classification.
  • Existing Twin Support Vector Regression (TSVR) formulations may not fully capture the TWSVM spirit.
  • Support Vector Regression (SVR) is a widely used regression technique.

Purpose of the Study:

  • To propose a new Twin Support Vector Regression (TWSVR) formulation that adheres to the principles of TWSVM.
  • To demonstrate that the proposed TWSVR can be derived from TWSVM for a classification problem.
  • To evaluate the performance of the proposed TWSVR against TSVR and SVR.

Main Methods:

  • Developing a novel TWSVR formulation inspired by Bi and Bennett (2003).
  • Establishing a theoretical link between the proposed TWSVR and TWSVM through classification.
  • Empirical comparison of TWSVR, TSVR, and SVR on diverse regression datasets.

Main Results:

  • The proposed TWSVR formulation is shown to be in the true spirit of TWSVM.
  • The efficacy of TWSVR is demonstrated through comparative analysis on regression datasets.
  • TWSVR exhibits competitive or superior performance compared to TSVR and SVR.

Conclusions:

  • The novel TWSVR formulation provides a theoretically sound and practically effective approach to regression.
  • This work advances the application of twin-support vector principles to regression problems.
  • The proposed TWSVR offers a valuable alternative for regression tasks in machine learning.