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Related Concept Videos

Associative Learning01:27

Associative Learning

Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
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Related Experiment Video

Updated: Jul 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Convergent on-line algorithms for supervised learning in neural networks.

L Grippo1

  • 1Dipartimento di Informatica e Sistemistica, Università di Roma La Sapienza, Italy. grippo@dis.uniroma1.it

IEEE Transactions on Neural Networks
|February 6, 2008
PubMed
Summary

This study introduces on-line algorithms for neural-network training using multiple network copies. These methods ensure reduced disagreement between networks and guarantee convergence, enabling real-time learning without forcing the learning rate to zero.

Related Experiment Videos

Last Updated: Jul 7, 2026

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Traditional neural network training often requires batch processing and can be slow.
  • Achieving stable convergence in neural network training is a persistent challenge.

Purpose of the Study:

  • To introduce novel on-line algorithms for efficient neural-network training.
  • To demonstrate guaranteed convergence and reduced network disagreement using multiple network copies.

Main Methods:

  • Developing on-line training algorithms based on multiple, concurrently trained network copies.
  • Utilizing distinct data blocks for training each network copy.
  • Analyzing algorithms to ensure asymptotic reduction in network disagreement.

Main Results:

  • The proposed algorithms guarantee convergence towards stationary points of the global error function.
  • The disagreement between different network copies is asymptotically reduced.
  • The learning rate is not necessarily forced to zero, enabling continuous learning.

Conclusions:

  • The developed on-line algorithms facilitate real-time neural network training.
  • This approach offers a robust method for achieving stable convergence in complex neural networks.
  • The flexibility in learning rate management enhances the practicality of these algorithms.