Subspace Learning Machine (SLM): A New Approach to Classification and Regression

Abstract

Classification and regression are some of the most important tasks handled by supervised machine learning. Many approaches like feedforward multilayer perceptron, decision tree, support vector machines, and extreme learning machine methods have been proposed in the past for these tasks. Recently, a new approach called subspace learning machine/regressor (SLM/SLR) has been applied to data with low to moderate dimensions and it has shown substantial advantages over other similar methods. This technical report describes SLM/SLR and traces the reasons behind its superior performance.

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Document Details

Document Type
Technical Report
Publication Date
Oct 01, 2022
Accession Number
AD1183920

Entities

People

  • C.-c. J. Kuo
  • Vinod K. Mishra

Organizations

  • United States Army Research Laboratory
  • University of Southern California

Tags

Fields of Study

  • Computer science

Readers

  • Computational Modeling and Simulation
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Neural Networks