Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Multiple model regression estimation.

Vladimir Cherkassky1, Yunqian Ma

  • 1Department of Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN 55455 USA. cherkass@ece.umn.edu

IEEE Transactions on Neural Networks
|August 27, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Unanticipated clustering-triggered emission in ammonium carboxylate salt structures.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy·2026
Same author

Electrochemical Sulfion Valorization Using Biphasic Electrolyte and Transition Metal Selenization Simultaneously Enabled by Ionic Liquid.

ChemSusChem·2025
Same author

Molecular dynamics and quantum chemical insights into cellulose dissolution in novel superbase-derived ionic liquid / organic cosolvent systems.

International journal of biological macromolecules·2025
Same author

Generalized Energy Band Alignment Model for van der Waals Heterostructures with a Charge Spillage Dipole.

ACS nano·2025
Same author

Efficient capture and conversion of H<sub>2</sub>S into H<sub>2</sub> and sulfur in guanidinium ionic liquid-NaOH aqueous solution.

Physical chemistry chemical physics : PCCP·2025
Same author

One-Step Soaking Approach for the Development of High-Strength, Tough Silicone Hydrogels with Elevated Polysiloxane Content.

ACS macro letters·2025
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

This study introduces a novel learning approach for multiple model estimation (MME), where data comes from various unknown statistical models. A new support vector machine (SVM) method is proposed for multiple regression estimation challenges.

Area of Science:

  • Machine Learning
  • Statistical Modeling
  • Data Science

Background:

  • Existing machine learning methods often assume data originates from a single statistical model.
  • This assumption is violated in scenarios with multiple underlying data-generating processes.
  • Handling data from multiple unknown models requires new estimation frameworks.

Purpose of the Study:

  • To propose a new learning formulation for multiple model estimation (MME).
  • To introduce a general framework applicable to MME problems.
  • To develop a specific methodology for multiple regression estimation within this framework.

Main Methods:

  • Developed a general framework for multiple model estimation.
  • Introduced a constructive support vector machine (SVM)-based methodology.

Related Experiment Videos

  • Utilized synthetic and real-life datasets for empirical validation.

    • Main Results:

    • Demonstrated the applicability of the proposed MME formulation.
    • Showcased the effectiveness of the SVM-based approach for multiple regression.
    • Empirical comparisons validated the proposed methodology.

    Conclusions:

    • The new learning formulation addresses limitations of single-model approaches in MME.
    • The proposed SVM-based method provides a viable solution for multiple regression estimation.
    • The approach is effective for diverse datasets, including real-world applications.