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

Second-order SMO improves SVM online and active learning.

Tobias Glasmachers1, Christian Igel

  • 1Institut für Neuroinformatik, Ruhr-Universität Bochum, Bochum, Germany. Tobias.Glasmachers@neuroinformatik.ruhr-uni-bochum.de

Neural Computation
|November 30, 2007
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

High-resolution automated mapping of potential Aedes larval container habitats using drone imagery and supervised machine learning in Dar es Salaam, Tanzania.

PLoS neglected tropical diseases·2026
Same author

Global daily 9 km remotely sensed soil moisture (2015-2025) with microwave radiative transfer-guided learning.

Scientific data·2026
Same author

Insights into motor control: predict muscle activity from upper limb kinematics with LSTM networks.

Scientific reports·2026
Same author

Hand Motion Catalog of Human Center-Out Transport Trajectories Measured Redundantly in 3D Task-Space.

Scientific data·2025
Same author

Absolute and relative risks of mental disorders in families: a Danish register-based study.

The lancet. Psychiatry·2025
Same author

Improving the Real-Time Classification of Disease Severity in Ulcerative Colitis: Artificial Intelligence as the Trigger for a Second Opinion.

The American journal of gastroenterology·2025
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

This study enhances iterative learning algorithms for Support Vector Machines (SVMs) by improving speed and accuracy. The new method uses a second-order working set selection for efficient large-scale machine learning.

Area of Science:

  • Machine Learning
  • Computational Statistics

Background:

  • Iterative algorithms approximate Support Vector Machine (SVM) solutions.
  • SVM approximations offer advantages in online/active learning and large datasets.
  • LASVM uses Sequential Minimal Optimization (SMO) for iterative SVM approximation.

Purpose of the Study:

  • To enhance the speed and accuracy of the LASVM algorithm.
  • To improve the efficiency of iterative SVM solutions for large datasets.

Main Methods:

  • Incorporated a second-order working set selection strategy into SMO steps.
  • Focused on greedily maximizing progress in each iterative step.

Main Results:

  • Achieved considerable improvements in both speed and accuracy compared to previous methods.

Related Experiment Videos

  • Demonstrated the effectiveness of the second-order working set selection strategy.

    • Conclusions:

    • The enhanced LASVM algorithm provides a more efficient and accurate approach to solving SVMs.
    • This improved method is particularly beneficial for large-scale machine learning tasks.