Jove
Visualize
Contact Us

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 Neurotransmitters01:30

Classification of Neurotransmitters

Neurotransmitters play a crucial role in the communication between neurons in the autonomic nervous system. Neurons in the autonomic nervous system can be cholinergic or adrenergic depending on the neurotransmitters synthesized. Cholinergic neurons use acetylcholine as their primary neurotransmitter. This includes all the preganglionic fibers of the sympathetic and pre- and postganglionic fibers of the parasympathetic nervous systems. In addition, neurons of the somatic nervous system also use...
Real-World Application of Classical Conditioning01:15

Real-World Application of Classical Conditioning

Classical conditioning not only includes the initial pairing of stimuli but also extends to more complex forms, such as higher-order conditioning. Higher-order conditioning involves creating associations beyond the primary conditioned stimulus, resulting in a chain of conditioned responses.
Higher-order, or second-order, conditioning occurs when a neutral stimulus becomes associated with an already established conditioned stimulus through repeated pairings. For instance, if a dog has been...
Classification of Signals01:30

Classification of Signals

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...

You might also read

Related Articles

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

Sort by
Same author

Radiation detection using diatomite: A natural approach to gamma-ray dosimetry.

Applied radiation and isotopes : including data, instrumentation and methods for use in agriculture, industry and medicine·2025
Same author

MOLECULAR DETECTION OF HIGH RISK HUMAN PAPILLOMA VIRUS SUBTYPES IN CERVICAL SMEARS AMONG SUDANESE WOMEN.

Georgian medical news·2025
Same author

ASSOCIATION BETWEEN MICROALBUMINURIA AND THYROID DYSFUNCTION COMPARED TO DIABETIC KIDNEY DISEASE PROGRESSION IN TYPE 2 DIABETES MELLITUS PATIENTS.

Georgian medical news·2025
Same author

UNVEILING LADA: PREVALENCE AND CHARACTERISTICS AMONG TYPE 2 DIABETIC PATIENTS IN PORT SUDAN, SUDAN.

Georgian medical news·2025
Same author

IMMUNOMETABOLIC CORRELATIONS OF AUTOANTIBODIES IN LATENT AUTOIMMUNE DIABETES IN ADULTS PATIENTS: A CROSS-SECTIONAL STUDY.

Georgian medical news·2025
Same author

SERUM TSH, FT3, FT4, AND FASTING BLOOD GLUCOSE LEVELS TO INVESTIGATE THE ASSOCIATION BETWEEN THYROID DYSFUNCTION AND TYPE 2 DIABETES MELLITUS.

Georgian medical news·2025
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 Experiment Video

Updated: Jul 7, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Neural classifiers and statistical pattern recognition: applications for currently established links.

H Osman1, M M Fahmy

  • 1Dept. of Electr. & Comput. Eng., Queen's Univ., Kingston, Ont.

IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics : a Publication of the IEEE Systems, Man, and Cybernetics Society
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

This study enhances backpropagation (BP) and radial basis function (RBF) networks in statistical pattern recognition by introducing new applications for discriminant analysis and generalization measures. These methods improve classifier performance and learning efficiency.

More Related Videos

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Related Experiment Videos

Last Updated: Jul 7, 2026

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms
08:51

Statistical Modelling of Cortical Connectivity Using Non-invasive Electroencephalograms

Published on: November 1, 2019

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Area of Science:

  • Machine Learning
  • Statistical Pattern Recognition
  • Artificial Neural Networks

Background:

  • Backpropagation (BP) and Radial Basis Function (RBF) networks are linked to Bayes decision theory and discriminant analysis.
  • Current applications for training, using, and evaluating these classifiers are limited.

Purpose of the Study:

  • To provide practical applications for BP and RBF networks in statistical pattern recognition.
  • To improve classification performance and learning efficiency for these network types.

Main Methods:

  • Utilizing the discriminant capability of linear output BP networks during training.
  • Defining and estimating a new generalization measure for linear output BP and RBF networks.
  • Proposing an upper bound for hidden units in RBF network classifiers.

Main Results:

  • Explicit use of discriminant capability improves BP network classification performance.
  • The new generalization measure serves as an efficient criterion for learning termination and network topology selection.
  • An upper bound on hidden units for RBF networks is established.

Conclusions:

  • The proposed methods offer practical advancements for BP and RBF network classifiers.
  • The generalization measure aids in optimizing network training and design.
  • Theoretical bounds on RBF network complexity are established, facilitating efficient model selection.