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

Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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...
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...
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,
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...

You might also read

Related Articles

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

Sort by
Same author

Precision Spectral Measurements of Chromium and Titanium from 10 to 250  GeV/n and Sub-Iron to Iron Ratio with the Calorimetric Electron Telescope on the International Space Station.

Physical review letters·2025
Same author

Direct Measurement of the Spectral Structure of Cosmic-Ray Electrons+Positrons in the TeV Region with CALET on the International Space Station.

Physical review letters·2023
Same author

Erratum: Charge-Sign Dependent Cosmic-Ray Modulation Observed with the Calorimetric Electron Telescope on the International Space Station [Phys. Rev. Lett. 130, 211001 (2023)].

Physical review letters·2023
Same author

Charge-Sign Dependent Cosmic-Ray Modulation Observed with the Calorimetric Electron Telescope on the International Space Station.

Physical review letters·2023
Same author

Direct Measurement of the Cosmic-Ray Helium Spectrum from 40 GeV to 250 TeV with the Calorimetric Electron Telescope on the International Space Station.

Physical review letters·2023
Same author

Cosmic-Ray Boron Flux Measured from 8.4  GeV/n to 3.8  TeV/n with the Calorimetric Electron Telescope on the International Space Station.

Physical review letters·2023

Related Experiment Video

Updated: Jul 7, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

Bayes statistical behavior and valid generalization of pattern classifying neural networks.

F Kanaya1, S Miyake

  • 1NTT Transmission Syst. Lab., Kanagawa.

IEEE Transactions on Neural Networks
|January 1, 1991
PubMed
Summary
This summary is machine-generated.

Neural networks trained with supervised learning create optimal empirical Bayes rules. With enough data, these rules approach the true Bayes optimal rule, enabling valid generalization from finite samples.

More Related Videos

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

Related Experiment Videos

Last Updated: Jul 7, 2026

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

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

Area of Science:

  • Machine Learning
  • Statistical Decision Theory
  • Pattern Recognition

Background:

  • Supervised learning algorithms are widely used for neural network pattern classification.
  • The Bayes rule offers theoretical optimality for classification tasks.
  • Understanding the generalization capabilities of neural networks is crucial.

Purpose of the Study:

  • To demonstrate that neural network pattern classifiers generate the empirical Bayes rule.
  • To show the asymptotic equivalence of network-generated rules to the theoretical Bayes rule.
  • To propose a probabilistic definition for valid generalization in neural networks.

Main Methods:

  • Theoretical analysis of supervised learning algorithms in neural networks.
  • Experimental validation of the generated empirical Bayes rule.
  • Application of the law of large numbers to analyze asymptotic behavior.

Main Results:

  • Neural network pattern classifiers implement the optimal empirical Bayes rule for the training sample distribution.
  • Asymptotic equivalence to the theoretical Bayes rule is established for large sample sizes.
  • A Bayes statistical decision framework provides a probabilistic definition of generalization.

Conclusions:

  • Supervised learning in neural networks naturally yields optimal empirical Bayes decision rules.
  • The generalization ability of neural networks is theoretically grounded in Bayes decision theory.
  • This work bridges the gap between empirical training and theoretical optimality in neural network classification.