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

Multiple Regression01:25

Multiple Regression

4.2K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
4.2K
Classification of Signals01:30

Classification of Signals

1.5K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.5K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

453
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
453
Regression Analysis01:11

Regression Analysis

8.7K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
8.7K
Classification of Systems-I01:26

Classification of Systems-I

647
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:
647
Classification of Systems-II01:31

Classification of Systems-II

540
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,
540

You might also read

Related Articles

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

Sort by
Same author

ALPK1 promotes cardiomyocyte hypertrophy by activating NF-κB/NLRP3 inflammasome-mediated pyroptosis.

Experimental cell research·2026
Same author

Excellent Response to 177 Lu-DOTA-IBA Therapy of Patients With Multiple Skeletal Metastases.

Clinical nuclear medicine·2026
Same author

Feasibility of a Randomized Controlled Trial Comparing Propofol and Sevoflurane General Anesthesia in Endovascular Thrombectomy for Stroke: A Pilot Study.

Journal of neurosurgical anesthesiology·2026
Same author

Biochemical and structural studies of NFIA and NFIC reveal a conserved mechanism for specific DNA recognition and provide insight into potential pathogenicity of disease-associated mutations.

Acta biochimica et biophysica Sinica·2025
Same author

Tackling inter-subject variability in smartwatch data using factorization models.

Scientific reports·2025
Same author

Head-to-head comparison of <sup>18</sup>F-FDG and <sup>68</sup>Ga-FAPI PET/CT in common gynecological malignancies.

Cancer imaging : the official publication of the International Cancer Imaging Society·2025
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: Mar 8, 2026

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
10:46

A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

Published on: December 9, 2015

11.2K

Multivariate Time-Series Classification Using the Hidden-Unit Logistic Model.

Wenjie Pei, Hamdi Dibeklioglu, David M J Tax

    IEEE Transactions on Neural Networks and Learning Systems
    |February 1, 2017
    PubMed
    Summary
    This summary is machine-generated.

    We introduce the hidden-unit logistic model (HULM) for multivariate time-series classification. This novel approach effectively models complex temporal dependencies and achieves state-of-the-art performance in various computer vision tasks.

    More Related Videos

    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

    596
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.6K

    Related Experiment Videos

    Last Updated: Mar 8, 2026

    A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
    10:46

    A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

    Published on: December 9, 2015

    11.2K
    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

    596
    Cross-Modal Multivariate Pattern Analysis
    13:51

    Cross-Modal Multivariate Pattern Analysis

    Published on: November 9, 2011

    20.6K

    Area of Science:

    • Machine Learning
    • Computer Vision
    • Artificial Intelligence

    Background:

    • Multivariate time-series classification is crucial for analyzing sequential data.
    • Existing models like hidden conditional random fields have limitations in modeling complex decision boundaries.
    • Latent structure and temporal dependencies are key challenges in time-series analysis.

    Purpose of the Study:

    • To introduce a novel model, the hidden-unit logistic model (HULM), for multivariate time-series classification.
    • To leverage binary stochastic hidden units for modeling latent data structures.
    • To improve the capability of modeling complex decision boundaries in time-series data.

    Main Methods:

    • Developed the hidden-unit logistic model (HULM) with a chain structure for hidden units.
    • Utilized binary stochastic hidden units to capture latent temporal dependencies.
    • Compared HULM against prior models like hidden conditional random fields.

    Main Results:

    • HULM demonstrates strong performance across diverse computer vision tasks.
    • Achieved state-of-the-art results in handwritten character recognition, speech recognition, facial expression, and action recognition.
    • Developed a state-of-the-art system for facial action unit detection using HULM.

    Conclusions:

    • The hidden-unit logistic model (HULM) offers superior performance in multivariate time-series classification.
    • HULM's architecture effectively models complex decision boundaries and temporal dependencies.
    • HULM provides a powerful new tool for various computer vision and time-series analysis applications.