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

The entire regularization path for the support vector domain description.

Karl Sjöstrand1, Rasmus Larsen

  • 1Informatics and Mathematical Modelling, Technical University of Denmark. kas@imm.dtu.dk

Medical Image Computing and Computer-Assisted Intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention
|March 16, 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

Deep learning models built from PSMA PET of the primary tumor can predict synchronous and metachronous prostate cancer metastases.

PloS one·2026
Same author

Automated Imaging as an Adjunct to Serum and Clinical Biomarkers: A New Validated Prediction Tool for Metastatic Castration-Resistant Prostate Cancer.

Clinical cancer research : an official journal of the American Association for Cancer Research·2025
Same author

Quark Mass Dependence of Heavy Quark Diffusion Coefficient from Lattice QCD.

Physical review letters·2024
Same author

High-Entropy Oxides in the Mullite-Type Structure.

Chemistry of materials : a publication of the American Chemical Society·2023
Same author

Heavy Quark Diffusion from 2+1 Flavor Lattice QCD with 320 MeV Pion Mass.

Physical review letters·2023
Same author

Multimodal soft tissue markers for bridging high-resolution diagnostic imaging with therapeutic intervention.

Science advances·2020
Same journal

LiftReg: Limited Angle 2D/3D Deformable Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Inverse Consistency by Construction for Multistep Deep Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Can Crowdsourced Annotations Improve AI-based Congestion Scoring For Bedside Lung Ultrasound?

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Equivariant Filters for Efficient Tracking in 3D Imaging.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

Lobar Lung Density Embeddings with a Transformer encoder (LobTe) to predict emphysema progression in COPD.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
Same journal

uniGradICON: A Foundation Model for Medical Image Registration.

Medical image computing and computer-assisted intervention : MICCAI ... International Conference on Medical Image Computing and Computer-Assisted Intervention·2026
See all related articles

Support vector domain description (SVDD) efficiently computes regularization paths for one-class classification. This enables more accurate models and new applications by analyzing data distributions and identifying outliers.

Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Data Analysis

Background:

  • Support Vector Domain Description (SVDD) is a one-class classification method for estimating data distribution shape.
  • SVDD separates data into inliers and outliers based on a decision boundary, similar to Support Vector Machines (SVMs).

Purpose of the Study:

  • To demonstrate that the piecewise linear regularization path property of SVMs extends to SVDD.
  • To enable efficient computation of SVDD solutions across a range of regularization parameters.

Main Methods:

  • Leveraging the piecewise linear regularization path property observed in SVMs.
  • Applying efficient computational methods to the SVDD regularization path.

Main Results:

Related Experiment Videos

  • The regularization path of SVDD is shown to be piecewise linear and efficiently computable.
  • Solutions for one-class classification can be obtained for any regularization amount with similar computational cost.

Conclusions:

  • The efficient computation of the entire SVDD regularization path offers more accurate and reliable models.
  • This advancement opens possibilities for novel applications in data analysis and outlier detection.