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

Force Classification01:22

Force Classification

2.2K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.2K
Classification of Systems-II01:31

Classification of Systems-II

439
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,
439
Classification of Systems-I01:26

Classification of Systems-I

515
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:
515
Aggregates Classification01:29

Aggregates Classification

929
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...
929
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

1.3K
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...
1.3K

You might also read

Related Articles

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

Sort by
Same author

MiR-335 inhibits migration of breast cancer cells through targeting oncoprotein c-Met.

Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine·2014
Same author

Dynamic ambulance reallocation for the reduction of ambulance response times using system status management.

The American journal of emergency medicine·2014
Same author

Sophorolipid production from biomass hydrolysates.

Applied biochemistry and biotechnology·2014
Same author

[Research on progress and prospect of kinase S6K1].

Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi·2014
Same author

Amplified fluorescent aptasensor through catalytic recycling for highly sensitive detection of ochratoxin A.

Biosensors & bioelectronics·2014
Same author

A novel artificial immune algorithm for spatial clustering with obstacle constraint and its applications.

Computational intelligence and neuroscience·2014
Same journal

Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

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

A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

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

Self-Supervised Continuous Dynamic Graph Representation Learning via Hawkes Processes.

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

cPU: Consistent Risk Estimator for Positive-Unlabeled Learning.

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

Tuning-Free Latent Diffusion Models for Ultrahigh-Resolution Image Editing.

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

Related Experiment Video

Updated: Jan 3, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

12.2K

Discriminative Fast Hierarchical Learning for Multiclass Image Classification.

Yu Zheng, Jianping Fan, Ji Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |November 22, 2019
    PubMed
    Summary
    This summary is machine-generated.

    A new discriminative fast hierarchical learning algorithm enhances multiclass image classification. This approach uses a visual tree and multitask learning for efficient, accurate, and computationally effective image categorization.

    More Related Videos

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.6K
    Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
    11:38

    Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

    Published on: October 4, 2024

    1.0K

    Related Experiment Videos

    Last Updated: Jan 3, 2026

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning
    14:38

    Creating Objects and Object Categories for Studying Perception and Perceptual Learning

    Published on: November 2, 2012

    12.2K
    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
    08:27

    Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

    Published on: January 5, 2024

    1.6K
    Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging
    11:38

    Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

    Published on: October 4, 2024

    1.0K

    Area of Science:

    • Computer Science
    • Machine Learning
    • Artificial Intelligence

    Background:

    • Multiclass image classification presents challenges in handling large datasets and data imbalance.
    • Hierarchical approaches can improve efficiency but often struggle with error propagation and task interdependencies.

    Purpose of the Study:

    • To develop a discriminative fast hierarchical learning algorithm for efficient multiclass image classification.
    • To integrate a visual tree structure with multitask learning to address data imbalance and identify interrelated tasks automatically.

    Main Methods:

    • A visual tree is constructed by hierarchically partitioning categories in a coarse-to-fine manner.
    • Multitask Support Vector Machine (SVM) classifiers are trained for non-leaf nodes to separate sibling child nodes.
    • Internode visual similarities and interlevel visual correlations are leveraged, utilizing a stochastic gradient descent (SGD) algorithm for efficient multitask SVM learning.

    Main Results:

    • The developed algorithm demonstrates competitive classification accuracy rates.
    • The approach achieves significant improvements in computational efficiency compared to existing methods.
    • Effective control of interlevel error propagation was observed.

    Conclusions:

    • The proposed fast hierarchical learning algorithm offers a robust solution for multiclass image classification.
    • The integration of visual trees and multitask learning effectively handles data imbalance and task interdependencies.
    • The algorithm provides a balance between high classification accuracy and computational efficiency.