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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

451
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...
451
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
Margin of Error01:27

Margin of Error

7.9K
The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
7.9K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

376
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
376
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

670
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
670
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

9.7K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
9.7K

You might also read

Related Articles

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

Sort by
Same author

Graph vector function architecture.

Neural networks : the official journal of the International Neural Network Society·2025
Same author

Editorial: Machine learning for cybersecurity.

Frontiers in artificial intelligence·2025
Same author

Binding in hippocampal-entorhinal circuits enables compositionality in cognitive maps.

Advances in neural information processing systems·2025
Same author

Preferences for Telephone Cancer Information and Support in People with Cancer and Carers: Attribute and Level Selection for a Discrete Choice Experiment.

The patient·2025
Same author

The neurobench framework for benchmarking neuromorphic computing algorithms and systems.

Nature communications·2025
Same author

The Challenges of Gender Diversity in Boards of Directors: An Australian Study with Global Implications.

Global challenges (Hoboken, NJ)·2025
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Related Experiment Video

Updated: Mar 7, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.6K

Multiclass Linear Perceptrons With Multiplicative Margins.

Dmitri Rachkovskij1,2, Evgeny Osipov3, Olexander Volkov4

  • 1Department of Computer Science, Electrical and Space Engineering, Luleå University of Technology, 971 87 Luleå, Sweden.

Neural Computation
|March 5, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces Multiplicative Margin Perceptron (MMPerc) classifiers, offering a novel approach to machine learning. MMPerc enhances classification confidence and typically outperforms standard perceptrons and other baselines.

Related Experiment Videos

Last Updated: Mar 7, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.6K

Area of Science:

  • Machine Learning
  • Classification Algorithms
  • Pattern Recognition

Background:

  • Standard perceptrons lack a margin mechanism, potentially leading to lower classification confidence.
  • Additive margin mechanisms can be sensitive to data and weight vector magnitudes.

Purpose of the Study:

  • Introduce a novel family of multiclass linear perceptron classifiers, Multiplicative Margin Perceptron (MMPerc).
  • Provide an alternative to margin-free and additive margin perceptrons with improved classification confidence.

Main Methods:

  • Propose architectural and algorithmic variants of MMPerc.
  • Derive loss functions and mistake bounds for separable and nonseparable data.
  • Analyze design considerations: bias, margin threshold, and training modes.

Main Results:

  • MMPerc classifiers demonstrate superior performance compared to standard perceptrons.
  • Experiments show MMPerc outperforms Support Vector Machines and ridge classifiers on synthetic and real datasets.
  • The multiplicative margin avoids dependence on score magnitudes.

Conclusions:

  • MMPerc classifiers offer simplicity, computational efficiency, and minimalistic design.
  • Promising for conventional machine learning, linear evaluation of deep networks, and resource-constrained applications.
  • Suitable for integration with hyperdimensional computing and vector symbolic architectures.