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

Neural Circuits01:25

Neural Circuits

Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
Implicit Differentiation: Problem Solving01:29

Implicit Differentiation: Problem Solving

Curves defined implicitly, where variables cannot be separated algebraically, require specialized techniques for analysis. The conchoid of Nicomedes exemplifies such a case. Its equation links x and y in a way that prevents isolation of one variable, making implicit differentiation essential to determine the slope and behavior at any point on the curve.The implicit form of the conchoid can be expressed as:To differentiate this equation, y is treated as a function of x, and the chain rule is...
Machines: Problem Solving II01:30

Machines: Problem Solving II

Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
Vector Functions and Motion: Problem Solving01:30

Vector Functions and Motion: Problem Solving

Accurate position tracking is fundamental to the safe and effective operation of unmanned aerial vehicles (UAVs), particularly during precision maneuvers near complex structures. In this scenario, a drone is programmed to perform a high-precision inspection of a vertical structure, starting at position ((x, y, z) = (3, 0, 0)), with an initial velocity oriented in the positive z-direction. The trajectory of the drone is governed by a time-dependent acceleration function a(t), which is predefined...
Lagrange Multipliers: Two Constraints01:28

Lagrange Multipliers: Two Constraints

The method of Lagrange multipliers with two constraints is used to optimize a function subject to two independent constraints. In many applications, the objective function represents a quantity to be maximized or minimized, such as cost, area, distance, or energy. The two constraints represent requirements that the solution must satisfy, such as fixed volume, limited resources, or prescribed dimensions.For a function of three variables, each constraint forms a surface in three-dimensional space.

You might also read

Related Articles

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

Sort by
Same author

Cost minimization analysis of capecitabine versus 5-fluorouracil-based treatment for gastric cancer patients in Hong Kong.

Journal of medical economics·2017
Same author

A 12-year review on the aetiology and surgical outcomes of paediatric rhegmatogenous retinal detachments in Hong Kong.

Eye (London, England)·2015
Same author

The preparedness of Hong Kong medical students towards advance directives and end-of-life issues.

East Asian archives of psychiatry : official journal of the Hong Kong College of Psychiatrists = Dong Ya jing shen ke xue zhi : Xianggang jing shen ke yi xue yuan qi kan·2012
Same author

Gender differences in social network characteristics and psychological well-being among Hong Kong Chinese: the role of future time perspective and adherence to Renqing.

Aging & mental health·2006
Same author

Impact of job characteristics on psychological health of Chinese single working women.

Women & health·2001
Same author

Constructive algorithms for structure learning in feedforward neural networks for regression problems.

IEEE transactions on neural networks·1997

Related Experiment Video

Updated: Jul 7, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Objective functions for training new hidden units in constructive neural networks.

T Y Kwok1, D Y Yeung

  • 1Dept. of Comput. Sci., Hong Kong Univ. of Sci. and Technol., Kowloon.

IEEE Transactions on Neural Networks
|January 1, 1997
PubMed
Summary
This summary is machine-generated.

This study introduces efficient objective functions for training neural networks, ensuring fast computation and preserved convergence even with input weight freezing. Practical optimization tricks are also discussed.

Related Experiment Videos

Last Updated: Jul 7, 2026

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments
03:31

End-To-End Deep Neural Network for Salient Object Detection in Complex Environments

Published on: December 15, 2023

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computational Neuroscience

Background:

  • Constructive algorithms for multilayer feedforward networks are essential for building complex neural networks.
  • Training new hidden units efficiently is a key challenge in constructive algorithms.
  • Existing methods may face computational complexity issues.

Purpose of the Study:

  • To derive objective functions for training hidden units in constructive algorithms.
  • To achieve O(N) time complexity for computation and weight updates, where N is the number of training patterns.
  • To maintain convergence properties despite using input weight freezing for efficiency.

Main Methods:

  • Development of a novel class of objective functions for hidden unit training.
  • Application of input weight freezing during the training process.
  • Analysis of computational complexity and convergence properties.
  • Introduction of computational tricks for practical optimization.

Main Results:

  • A class of objective functions with O(N) computation and weight update time was derived.
  • Convergence properties of constructive algorithms were preserved even with input weight freezing.
  • The performance of the proposed methods was evaluated on two-dimensional regression problems.

Conclusions:

  • The proposed objective functions offer an efficient approach to training hidden units in constructive neural network algorithms.
  • The methods balance computational efficiency with preserved convergence.
  • The findings provide practical insights for optimizing neural network training.