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

Survival Tree01:19

Survival Tree

132
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
132
Classification of Systems-I01:26

Classification of Systems-I

270
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:
270
Classification of Systems-II01:31

Classification of Systems-II

214
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,
214
Phylogenetic Trees03:21

Phylogenetic Trees

45.9K
Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
45.9K
Observational Learning01:12

Observational Learning

269
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
269
Introduction to Learning01:18

Introduction to Learning

511
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...
511

You might also read

Related Articles

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

Sort by
Same author

Letter to the Editor "The dynamic changes and precise classification of parathyroid function within one year after thyroid cancer surgery".

International journal of surgery (London, England)·2026
Same author

Breast cancer risk associated with <i>BRCA1</i> and <i>BRCA2</i> pathogenic variants in the Eastern Chinese population.

Cancer pathogenesis and therapy·2025
Same author

A review of advances in in vitro RNA preparation by ssRNAP.

International journal of biological macromolecules·2025
Same author

Dynamic estimates of survival of patients with poorly differentiated thyroid carcinoma: a population-based study.

Frontiers in endocrinology·2024
Same author

AI-based tree modeling for multi-point dioxin concentrations in municipal solid waste incineration.

Journal of hazardous materials·2024
Same author

Unveiling dioxin dynamics: A whole-process simulation study of municipal solid waste incineration.

The Science of the total environment·2024
Same journal

Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

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

Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

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

HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

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

Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

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

Related Experiment Video

Updated: Aug 23, 2025

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

3.4K

Tree Broad Learning System for Small Data Modeling.

Heng Xia, Jian Tang, Wen Yu

    IEEE Transactions on Neural Networks and Learning Systems
    |November 3, 2022
    PubMed
    Summary
    This summary is machine-generated.

    Tree-based Broad Learning System (TBLS) enhances small data modeling efficiency by replacing traditional neurons with tree modules. This novel approach significantly improves modeling accuracy, especially with limited training data, outperforming existing methods.

    More Related Videos

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.6K
    A Practical Guide to Phylogenetics for Nonexperts
    12:00

    A Practical Guide to Phylogenetics for Nonexperts

    Published on: February 5, 2014

    35.4K

    Related Experiment Videos

    Last Updated: Aug 23, 2025

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
    04:35

    Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

    Published on: July 3, 2020

    3.4K
    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
    12:18

    A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

    Published on: January 11, 2020

    7.6K
    A Practical Guide to Phylogenetics for Nonexperts
    12:00

    A Practical Guide to Phylogenetics for Nonexperts

    Published on: February 5, 2014

    35.4K

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Data Science

    Background:

    • Traditional Broad Learning System based on Neural Network (BLS-NN) exhibits limitations in efficiency for small datasets across various dimensions.
    • There is a need for advanced modeling techniques that can effectively handle high-dimensional and limited data scenarios.

    Purpose of the Study:

    • To introduce and evaluate a novel Tree-based Broad Learning System (TBLS) for improved small data modeling.
    • To explore the efficacy of nondifferentiable modules and ensemble strategies within the BLS framework.
    • To present and implement three new TBLS variants with distinct incremental learning strategies.

    Main Methods:

    • TBLS replaces standard neurons with tree modules for input data mapping.
    • Three incremental learning strategies are proposed: mean square error (MSE), pseudo-inverse, and pseudo-inverse theory with stack representation.
    • The proposed TBLS methods are compared against state-of-the-art BLS-NN and other tree-based methods.

    Main Results:

    • TBLS demonstrates superior performance compared to BLS-NN across high-, medium-, and low-dimensional datasets.
    • Remarkable improvements in modeling accuracy are achieved using TBLS, particularly with small training datasets.
    • The proposed incremental learning strategies contribute to the enhanced efficiency and accuracy of TBLS.

    Conclusions:

    • TBLS offers a significant advancement in modeling efficiency and accuracy for small, high-dimensional datasets.
    • The integration of nondifferentiable modules and ensemble strategies in TBLS provides a robust framework for complex data modeling challenges.
    • TBLS represents a promising direction for extending the capabilities of Broad Learning Systems.