Jove
Visualize
Contact Us

Related Concept Videos

Generalized Hooke's Law01:22

Generalized Hooke's Law

2.7K
The generalized Hooke's Law is a broadened version of Hooke's Law, which extends to all types of stress and in every direction. Consider an isotropic material shaped into a cube subjected to multiaxial loading. In this scenario, normal stresses are exerted along the three coordinate axes. As a result of these stresses, the cubic shape deforms into a rectangular parallelepiped. Despite this deformation, the new shape maintains equal sides, and there is a normal strain in the direction of the...
2.7K
Generalized Anxiety Disorder01:30

Generalized Anxiety Disorder

704
Generalized Anxiety Disorder (GAD) is a chronic condition characterized by excessive and uncontrollable worry that persists for at least six months, significantly interfering with daily functioning. Unlike situational anxiety, which arises in response to specific stressors, GAD often occurs without a clear cause. Individuals may experience disproportionate worry about work, health, or relationships. For instance, a person might continuously fear poor health despite normal medical evaluations or...
704
Social Foundations of Self II: The Generalized Other01:20

Social Foundations of Self II: The Generalized Other

261
According to George Herbert Mead, as children progress beyond the game stage, they develop a more comprehensive understanding of societal rules and norms. This cognitive and social development enables them to internalize the expectations of the broader community, refining their ability to regulate behavior.Consistent participation in organized activities is crucial in helping children recognize that their actions are not isolated but contribute to a more significant, interconnected group...
261
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.4K
Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
1.4K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

2.6K
Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
2.6K
Multiple Allele Traits01:49

Multiple Allele Traits

38.1K
The Concept of Multiple Allelism
38.1K

You might also read

Related Articles

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

Sort by
Same author

Random forest-integrated analysis in AD and LATE brain transcriptome-wide data to identify disease-specific gene expression.

PloS one·2021
Same journal

RETRACTION: Multidimensional Heterogeneous Network Link Adaptation Based on Mobile Environment.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Framework to Segment and Evaluate Multiple Sclerosis Lesion in MRI Slices Using VGG-UNet.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Facial Emotion Recognition Using a Novel Fusion of Convolutional Neural Network and Local Binary Pattern in Crime Investigation.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Automatic Intelligent System Using Medical of Things for Multiple Sclerosis Detection.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: Intangible Cultural Heritage Reproduction and Revitalization: Value Feedback, Practice, and Exploration Based on the IPA Model.

Computational intelligence and neuroscience·2026
Same journal

RETRACTION: CNN Based Multiclass Brain Tumor Detection Using Medical Imaging.

Computational intelligence and neuroscience·2025
See all related articles
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 Video

Updated: Feb 1, 2026

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays
10:01

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays

Published on: April 23, 2012

18.7K

Generalization Bounds for Coregularized Multiple Kernel Learning.

Xinxing Wu1, Guosheng Hu1

  • 1Department of Communication and Information Engineering, Shanghai Technical Institute of Electronics & Information, Shanghai 201411, China.

Computational Intelligence and Neuroscience
|December 6, 2018
PubMed
Summary
This summary is machine-generated.

This study analyzes multiple kernel learning (MKL) within semisupervised multiview learning. Researchers derived a generalization error bound for coregularized MKL using Rademacher complexity, unifying prior work.

More Related Videos

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.2K
Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation
08:04

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Published on: August 23, 2017

8.7K

Related Experiment Videos

Last Updated: Feb 1, 2026

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays
10:01

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays

Published on: April 23, 2012

18.7K
Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective
13:57

Assessing the Multiple Dimensions of Engagement to Characterize Learning: A Neurophysiological Perspective

Published on: July 1, 2015

13.2K
Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation
08:04

Using a Split-belt Treadmill to Evaluate Generalization of Human Locomotor Adaptation

Published on: August 23, 2017

8.7K

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Statistical Learning Theory

Background:

  • Multiple Kernel Learning (MKL) is crucial for automated kernel selection in machine learning.
  • Existing learning theories primarily focus on MKL generalization, with limited research in semisupervised settings.
  • Semisupervised multiview learning presents unique challenges for MKL analysis.

Purpose of the Study:

  • To analyze the generalization of multiple kernel learning within the semisupervised multiview learning framework.
  • To derive a generalization error bound for coregularized multiple kernel learning.
  • To demonstrate that existing MKL and coregularized kernel learning results are special cases of the proposed framework.

Main Methods:

  • Application of Rademacher chaos complexity to control the performance of coregularized multiple kernels.
  • Theoretical analysis of generalization error bounds in semisupervised multiview learning.
  • Formulation of a unified framework encompassing existing MKL and coregularized kernel learning results.

Main Results:

  • A novel generalization error bound for coregularized multiple kernel learning in semisupervised multiview settings was obtained.
  • The proposed method provides a theoretical guarantee for the performance of coregularized MKL.
  • The derived results encompass and generalize previous findings in multiple kernel learning.

Conclusions:

  • The study advances the theoretical understanding of multiple kernel learning in semisupervised multiview learning.
  • The derived generalization error bound offers valuable insights for designing effective MKL algorithms.
  • This work establishes a unified theoretical foundation for various kernel learning approaches.