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 Systems-I01:26

Classification of Systems-I

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:
Classification of Systems-II01:31

Classification of Systems-II

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,
Perception01:28

Perception

Perception is a fundamental psychological process that enables individuals to organize, interpret, and consciously experience sensory information. This process is crucial for understanding and interacting with the world around us. It includes both bottom-up and top-down processing, each playing a distinct role in how we perceive our environment.
Bottom-up processing begins at the sensory level, where receptors detect external environmental stimuli. These could include the tactile sensation of...
Methods of Classification and Identification01:28

Methods of Classification and Identification

Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
Gestalt Principles of Perception01:21

Gestalt Principles of Perception

Gestalt principles provide a framework for understanding how humans perceive objects as unified wholes within their context. These principles are essential in explaining the cognitive processes that make sense of complex visual stimuli by organizing them into coherent groups. One fundamental principle is proximity, which posits that objects located close to each other are perceived as a collective group. For instance, when dots are positioned near one another, the visual system interprets them...
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...

You might also read

Related Articles

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

Sort by
Same author

Twelve years of genomic surveillance of vancomycin-resistant Enterococcus faecium: emergence of linear vanA and bacteriocin-carrying plasmids challenging infection control.

Genome medicine·2026
Same author

Authors' Reply to 'Methodological Considerations for Surveys of Dental Students' Knowledge and Attitudes Towards Artificial Intelligence in Oral Cancer Diagnosis'.

Oral diseases·2026
Same author

Knowledge, Attitudes, and Perceptions of Evidence-Based Dentistry Among Final-Year Dental Students: A Multinational Study Across 6 Countries and 8 Universities.

European journal of dental education : official journal of the Association for Dental Education in Europe·2026
Same author

Testing the redox theory of aging under parasitism.

npj aging·2026
Same author

Integrating the Microbiome Into Infection Ecology and Evolution in Wild Animals.

Molecular ecology·2026
Same author

Dental Students' Knowledge, Attitudes and Perceptions of Artificial Intelligence Tools to Aid in the Diagnosis of Oral Cancer and Oral Potentially Malignant Disorders.

Oral diseases·2026
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: Jun 12, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

The MEE principle in data classification: a perceptron-based analysis.

Luís M Silva1, J Marques de Sá, Luís A Alexandre

  • 1Instituto de Engenharia Biomédica, Divisão de Sinal e Imagem, Porto, Portugal. lmsilva@fe.up.pt

Neural Computation
|June 24, 2010
PubMed
Summary
This summary is machine-generated.

Information-theoretic risk functionals, like Shannon entropy, can achieve minimum probability of error in data classification. This study clarifies how parameters in perceptron activation functions and error density estimation enable this minimization of error entropy (MEE) principle.

More Related Videos

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Related Experiment Videos

Last Updated: Jun 12, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
09:34

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data

Published on: September 25, 2021

Area of Science:

  • Machine Learning
  • Information Theory
  • Computational Neuroscience

Background:

  • Data classification relies on minimizing error rates.
  • Information-theoretic principles offer alternative risk functionals for classification.
  • The minimization of error entropy (MEE) principle is a proposed approach.

Purpose of the Study:

  • To investigate if information-theoretic risk functionals can attain the minimum probability of error in data classification.
  • To analyze the minimization of error entropy (MEE) principle within a single perceptron model.
  • To clarify the theoretical and practical aspects of MEE in achieving optimal classification error.

Main Methods:

  • Analysis of a single perceptron with continuous activation functions.
  • Examination of continuous error distributions.
  • Investigation of parameters controlling squashing-type activation functions.
  • Evaluation of kernel density estimators for error density.

Main Results:

  • The study reveals diverse behaviors of the MEE principle.
  • Theoretical analysis clarifies how perceptron activation function parameters contribute to achieving minimum error.
  • Practical analysis highlights the role of kernel density estimation in reaching minimum error.

Conclusions:

  • Information-theoretic risk functionals, specifically MEE, can effectively minimize classification error.
  • Perceptron activation function parameters and error density estimation are crucial for practical MEE implementation.
  • The findings provide theoretical and practical insights into MEE for data classification.