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 Concept Videos

Concepts and Prototypes01:24

Concepts and Prototypes

The human nervous system handles vast amounts of information by translating sensory stimuli into neural impulses, which the brain processes, creating thoughts expressed through language or stored as memories. The brain also synthesizes information from emotions and memories, which significantly influence thoughts and behaviors. This intricate process creates a comprehensive mental picture.
The brain organizes this information using concepts, which are mental categories grouping linguistic data,...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Classification of Systems-II01:31

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
Stereotype Content Model02:16

Stereotype Content Model

The Stereotype Content Model (SCM) was first proposed by Susan Fiske and her colleagues (Fiske, Cuddy, Glick & Xu, 2002; see also Fiske, 2012 and Fiske, 2017). The SCM specifies that when someone encounters a new group, they will stereotype them based on two metrics: warmth—or that group’s perceived intent, and how likely they are to provide help or inflict harm—and competence—or their ability to carry out that objective. Depending on the warmth-competence categorization, a person will feel...
Response Surface Methodology01:16

Response Surface Methodology

Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
Aggregates Classification01:29

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

You might also read

Related Articles

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

Sort by
Same author

Towards robust foundation models for digital pathology.

Nature communications·2026
Same author

Beyond attention heatmaps: How to get better explanations for multiple instance learning models in histopathology.

Medical image analysis·2026
Same author

AI-based discovery of functional boundaries in the human brain from intraoperative electrophysiology.

medRxiv : the preprint server for health sciences·2026
Same author

Modeling attention and binding in the brain through bidirectional recurrent gating.

Nature communications·2026
Same author

A robust and versatile deep learning model for prediction of the arterial input function in dynamic small animal [<sup>18</sup>F] FDG PET imaging.

EJNMMI research·2026
Same author

How simple can you go? An off-the-shelf transformer approach to molecular dynamics.

The Journal of chemical physics·2026
Same journal

Modeling and analysis of forward and inverse kinematics for a flexible Stewart platform.

PloS one·2026
Same journal

Barriers and facilitators to healthcare utilization amongst people living with sickle cell disease in the United States: A scoping review.

PloS one·2026
Same journal

Enhancing data completeness in time series: Imputation strategies for missing data using significant periodically correlated components.

PloS one·2026
Same journal

Key targets and mechanisms by which gut microbiota-derived metabolites regulate Alzheimer's disease through the immune - inflammatory pathway: Based on network pharmacology and molecular docking.

PloS one·2026
Same journal

Grid-tied Transformer-less Boost Switched Capacitor Topology (TLBSCT) for PV applications.

PloS one·2026
Same journal

The load-velocity profiles and exercise-specific velocity zones for seven commonly used weightlifting exercises.

PloS one·2026
See all related articles

Related Experiment Video

Updated: May 17, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

A scatter-based prototype framework and multi-class extension of support vector machines.

Robert Jenssen1, Marius Kloft, Alexander Zien

  • 1Department of Physics and Technology, University of Tromsø, Tromsø, Norway. robert.jenssen@uit.no

Plos One
|November 3, 2012
PubMed
Summary
This summary is machine-generated.

This study introduces a novel Scatter Support Vector Machine (SVM) algorithm for multi-class classification. The new method offers efficient computation and improved generalization, providing a new interpretation of SVM duals.

Related Experiment Videos

Last Updated: May 17, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Machine Learning
  • Pattern Recognition
  • Computational Statistics

Background:

  • Support Vector Machines (SVMs) are powerful classification algorithms.
  • Existing multi-class SVM methods can be computationally intensive.
  • A novel interpretation of SVM duals is needed for improved efficiency.

Purpose of the Study:

  • To develop a new multi-class classification algorithm based on scatter SVM.
  • To provide an efficient computational framework for multi-class SVM.
  • To analyze the generalization ability and properties of the proposed algorithm.

Main Methods:

  • A novel interpretation of SVM duals using scatter with respect to class prototypes and their mean.
  • Extension of the scatter SVM framework to multiple classes, creating a joint Scatter SVM algorithm.
  • Development of computationally efficient solvers using sequential minimal and chunking optimization.
  • Primal problem formulation in terms of regularized risk minimization and hinge loss.

Main Results:

  • The proposed joint Scatter SVM algorithm has an equivalent number of optimization variables as its binary counterpart.
  • Computationally efficient solvers were implemented.
  • The score function for test pattern classification was revealed.
  • Promising results were obtained regarding generalization ability, computational efficiency, sparsity, and sensitivity maps.

Conclusions:

  • The novel joint Scatter SVM algorithm offers an efficient and effective approach to multi-class classification.
  • The framework provides new insights into SVM duals and their properties.
  • The method demonstrates potential for improved performance in pattern recognition tasks.