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

504
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...
504
Classification of Systems-II01:31

Classification of Systems-II

585
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,
585
Classification of Systems-I01:26

Classification of Systems-I

712
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:
712
Aggregates Classification01:29

Aggregates Classification

1.2K
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...
1.2K

You might also read

Related Articles

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

Sort by
Same author

Fulminant Type 1 Diabetes Mellitus Associated With Drug Hypersensitivity and Epstein-Barr Virus Infection: A Case Report.

Frontiers in pharmacology·2022
Same author

The expression and diagnostic value of serum levels of EphA2 and VEGF-A in patients with colorectal cancer.

Cancer biomarkers : section A of Disease markers·2021
Same author

Transition-metal-free C(sp<sup>3</sup>)-H/C(sp<sup>3</sup>)-H dehydrogenative coupling of saturated heterocycles with <i>N</i>-benzyl imines.

Chemical science·2021
Same author

Nickel-catalyzed enantioselective vinylation of aryl 2-azaallyl anions.

Chemical science·2021
Same author

Genetic Association Study Revealed Three Loci Were Associated Risk of Myopia Among Minors.

Pharmacogenomics and personalized medicine·2021
Same author

Comment on: Health-related quality of life following total minimally invasive, hybrid minimally invasive or open oesophagectomy: a population-based cohort study.

The British journal of surgery·2021

Related Experiment Video

Updated: Apr 20, 2026

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

7.5K

Discriminative Hierarchical K-Means Tree for Large-Scale Image Classification.

Shizhi Chen, Xiaodong Yang, Yingli Tian

    IEEE Transactions on Neural Networks and Learning Systems
    |November 25, 2014
    PubMed
    Summary

    We introduce a novel discriminative hierarchical K-means tree (D-HKTree) for efficient large-scale image classification. This method significantly reduces computation and memory costs while maintaining high accuracy, outperforming existing approaches.

    Related Experiment Videos

    Last Updated: Apr 20, 2026

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
    12:27

    Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

    Published on: February 15, 2017

    7.5K

    Area of Science:

    • Computer Science
    • Machine Learning
    • Pattern Recognition

    Background:

    • Large-scale image classification faces challenges in balancing computational efficiency, memory usage, and accuracy.
    • Learning-based classifiers offer high accuracy but suffer from linear computational complexity with class count.
    • Nonparametric nearest neighbor (NN)-based classifiers handle many categories but are computationally and memory-intensive.

    Purpose of the Study:

    • To develop a novel classification scheme that combines the strengths of learning-based and NN-based classifiers.
    • To address the limitations of existing methods in terms of computational complexity and memory requirements for large-scale image classification.

    Main Methods:

    • Introduction of the discriminative hierarchical K-means tree (D-HKTree) classification scheme.
    • The D-HKTree integrates advantages from both learning-based and nearest neighbor (NN)-based approaches.
    • Hierarchical structure designed for sublinear complexity growth with the number of categories.

    Main Results:

    • The D-HKTree exhibits sublinear complexity growth concerning the number of categories, surpassing hierarchical support vector machines.
    • Achieved memory requirements are significantly lower than recent Naïve Bayesian NN-based methods.
    • Demonstrated state-of-the-art accuracies on benchmark databases with substantially reduced computation and memory costs.

    Conclusions:

    • The D-HKTree offers a highly efficient solution for large-scale image classification.
    • This novel approach effectively reduces computational and memory demands without sacrificing classification performance.
    • The D-HKTree represents a significant advancement over existing hierarchical and NN-based classification techniques.