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

Limits to Natural Selection01:38

Limits to Natural Selection

35.7K
Organisms that are well-adapted to their environment are more likely to survive and reproduce. However, natural selection does not lead to perfectly adapted organisms. Several factors constrain natural selection.
35.7K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

382
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
382
Evolutionary Psychology01:20

Evolutionary Psychology

1.1K
Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
1.1K
Introduction to Learning01:18

Introduction to Learning

1.4K
Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
1.4K
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

3.0K
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...
3.0K
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.7K
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...
1.7K

You might also read

Related Articles

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

Sort by
Same author

Progressive Fusion of Multi-Scale Mamba Context and Local Detail Priors for Infrared Small Target Detection.

IEEE transactions on image processing : a publication of the IEEE Signal Processing Society·2026
Same author

Performance of Age-Adjusted Whole Genome Sequencing Telomere Length in Idiopathic Pulmonary Fibrosis.

American journal of respiratory and critical care medicine·2026
Same author

Publisher Correction: Whole genome sequence analysis of pulmonary function and COPD in 44,287 multi-ancestry participants.

Genome biology·2026
Same author

Optical Coherence Tomography Biomarkers Differentiate Epiretinal Membranes Secondary to Retinal Detachment from Idiopathic Epiretinal Membranes.

Journal of vitreoretinal diseases·2026
Same author

Arrhythmia Burden and Clinical Responses Under Continuous Monitoring in Heart Failure: Observations From the ALLEVIATE-HF Trial.

Journal of the American College of Cardiology·2026
Same author

Risk-Based Nurse-Managed Personalized Heart Failure Interventions: The ALLEVIATE-HF Trial.

Journal of the American College of Cardiology·2026
Same journal

Hidden Data Recovery and Forecasting via Next-Generation Reservoir Computing With Multiscale Delay Selection.

IEEE transactions on neural networks and learning systems·2026
Same journal

CAFF-CIL: Causality-Aware Freedom Forgetting Approach for Class-Incremental Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Harmonic Autoencoding Framework for Multiple Tasks in Magnetic Particle Imaging Reconstruction.

IEEE transactions on neural networks and learning systems·2026
Same journal

A Survey on Human-Centric Voice-Face Multimodal Learning.

IEEE transactions on neural networks and learning systems·2026
Same journal

Vision-Assisted Foundation Model for Solving Multitask Vehicle Routing Problems.

IEEE transactions on neural networks and learning systems·2026
Same journal

FP3O: Enabling Proximal Policy Optimization in Multiagent Cooperation With Parameter-Sharing Versatility.

IEEE transactions on neural networks and learning systems·2026
See all related articles

Related Experiment Video

Updated: Jun 14, 2026

Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

Evolutionary Cost-Sensitive Extreme Learning Machine.

Lei Zhang, David Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |October 15, 2016
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an evolutionary cost-sensitive Extreme Learning Machine (ELM) to address unknown misclassification costs in recognition tasks. This novel approach optimizes the cost matrix, improving performance in sensitive applications.

    Related Experiment Videos

    Last Updated: Jun 14, 2026

    Constructing and Visualizing Models using Mime-based Machine-learning Framework
    06:19

    Constructing and Visualizing Models using Mime-based Machine-learning Framework

    Published on: July 22, 2025

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Pattern Recognition

    Background:

    • Conventional Extreme Learning Machines (ELMs) assume uniform misclassification costs, which is unsuitable for cost-sensitive tasks like face recognition.
    • Real-world scenarios often involve unknown cost matrices, limiting the effectiveness of standard cost-sensitive learning methods.

    Purpose of the Study:

    • To propose the first evolutionary cost-sensitive Extreme Learning Machine (ELM) for classification tasks with unknown misclassification costs.
    • To address the challenge of defining and optimizing cost matrices in cost-sensitive learning.

    Main Methods:

    • Developed an evolutionary cost-sensitive ELM framework.
    • Introduced an evolutionary backtracking search algorithm for adaptive cost matrix optimization.
    • Evaluated the approach on various cost-sensitive tasks.

    Main Results:

    • The proposed evolutionary cost-sensitive ELM effectively handles unknown cost matrices.
    • Demonstrated significant performance improvements, achieving approximately 5%-10% gains in experimental tasks.
    • Successfully optimized adaptive cost matrices using the evolutionary algorithm.

    Conclusions:

    • The novel evolutionary cost-sensitive ELM offers a robust solution for classification problems with asymmetric misclassification costs.
    • This method provides a practical way to define and optimize cost matrices, enhancing recognition system reliability.