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

Classification of Neurotransmitters01:30

Classification of Neurotransmitters

5.1K
Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
5.1K
Classification of Leukocytes01:30

Classification of Leukocytes

5.0K
Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...
5.0K
Classification of Bones01:18

Classification of Bones

9.6K
The bones of the human skeletal system are of varied shapes, sizes, and functions. They can be classified based on their shape and function into four major classes: long bones, short bones, flat bones, and irregular bones. Some classifications include a fifth type, the sesamoid bones, as a separate class, whereas others categorize them under short bones.
Long and Short Bones
The appendicular skeleton, particularly the upper and lower limbs, is primarily made of long and short bones. The...
9.6K
Force Classification01:22

Force Classification

2.3K
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,...
2.3K
Classification of Illness01:17

Classification of Illness

8.6K
The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
8.6K
Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)01:20

Spin–Spin Coupling: Two-Bond Coupling (Geminal Coupling)

1.6K
Two NMR-active nuclei bonded to a central atom can be involved in geminal or two-bond coupling. Geminal coupling is commonly seen between diastereotopic protons in chiral molecules and unsymmetrical alkenes, among others.
The central atom need not be NMR-active because its electrons are affected by the electron polarization of the spin-active atoms. However, spin information is transmitted less effectively than in one-bond coupling, and 2J values are usually weaker than 1J values. The energy of...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Huang Qi Decoction Prevents BDL-Induced Liver Fibrosis Through Inhibition of Notch Signaling Activation.

The American journal of Chinese medicine·2017
Same author

Astragaloside IV Attenuates Podocyte Apoptosis Mediated by Endoplasmic Reticulum Stress through Upregulating Sarco/Endoplasmic Reticulum Ca<sup>2+</sup>-ATPase 2 Expression in Diabetic Nephropathy.

Frontiers in pharmacology·2017
Same author

A bio-chemical application of N-GQDs and g-C<sub>3</sub>N<sub>4</sub> QDs sensitized TiO<sub>2</sub> nanopillars for the quantitative detection of pcDNA3-HBV.

Biosensors & bioelectronics·2017
Same author

Clinical and imaging analysis of subclinical hemophilia combined with coxarthrosis: case report and literature review.

SpringerPlus·2016
Same author

On the summertime air quality and related photochemical processes in the megacity Shanghai, China.

The Science of the total environment·2016
Same author

Cuticular Wax Accumulation Is Associated with Drought Tolerance in Wheat Near-Isogenic Lines.

Frontiers in plant science·2016

Related Experiment Video

Updated: Jan 20, 2026

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
12:06

Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

Published on: March 3, 2023

4.7K

Semi-Supervised Deep Coupled Ensemble Learning with Classification Landmark Exploration.

Jichang Li, Si Wu, Cheng Liu

    IEEE Transactions on Image Processing : a Publication of the IEEE Signal Processing Society
    |August 20, 2019
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a Deep Coupled Ensemble (DCE) model for semi-supervised learning, enhancing deep learning stability and performance. The DCE model effectively utilizes unlabeled data to improve classification accuracy and decision boundaries.

    More Related Videos

    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
    09:16

    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

    Published on: June 18, 2020

    7.3K
    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
    06:22

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

    Published on: September 19, 2025

    441

    Related Experiment Videos

    Last Updated: Jan 20, 2026

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.7K
    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis
    09:16

    Supervised Machine Learning for Semi-Quantification of Extracellular DNA in Glomerulonephritis

    Published on: June 18, 2020

    7.3K
    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
    06:22

    Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

    Published on: September 19, 2025

    441

    Area of Science:

    • Machine Learning
    • Deep Learning
    • Computer Science

    Background:

    • Ensemble methods improve deep learning performance and stability over single networks.
    • Semi-supervised learning leverages limited labeled data with abundant unlabeled data.

    Purpose of the Study:

    • To introduce a Deep Coupled Ensemble (DCE) model for semi-supervised learning.
    • To enhance classification landmark exploration and decision boundary localization.
    • To improve the efficiency and performance of deep learning models.

    Main Methods:

    • Integrating multiple complementary consistency regularizations for inter-member learning.
    • Employing class-wise mean feature matching to identify reliable classification landmarks in unlabeled data.
    • Minimizing weighted conditional entropy on unlabeled data to refine decision boundaries.

    Main Results:

    • The DCE model demonstrates superior performance compared to state-of-the-art semi-supervised methods.
    • Consistency regularization ensures similar performance across ensemble members.
    • The model achieves efficiency comparable to non-ensemble methods during testing.

    Conclusions:

    • The proposed Deep Coupled Ensemble (DCE) model is highly effective for semi-supervised learning.
    • DCE enhances deep learning by improving stability, performance, and decision boundary accuracy.
    • The method offers a significant advancement in leveraging unlabeled data for classification tasks.