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 Experiment Videos

Data smoothing regularization, multi-sets-learning, and problem solving strategies.

Lei Xu1

  • 1Department of Computer Science and Engineering, Chinese University of Hong Kong, Shatin, NT, Hong Kong, China. lxu@cse.cuhk.edu.hk

Neural Networks : the Official Journal of the International Neural Network Society
|July 10, 2003
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

Topological phase transition induced by spin-orbit coupling in bilayer graphene.

Journal of physics. Condensed matter : an Institute of Physics journal·2013
Same author

[Survey on the occupational musculoskeletal disorder and its risk factors among male steelworkers].

Zhonghua yu fang yi xue za zhi [Chinese journal of preventive medicine]·2013
Same author

Characterisation and identification of dihydroindole-type alkaloids from processed semen strychni by high-performance liquid chromatography coupled with electrospray ionisation ion trap time-of-flight mass spectrometry.

Phytochemical analysis : PCA·2013
Same author

Identification and effect decomposition of risk factors for Brucella contamination of raw whole milk in china.

PloS one·2013
Same author

Different toxicity of the novel Bacillus thuringiensis (Bacillales: Bacillaceae) strain LLP29 against Aedes albopictus and Culex quinquefasciatus (Diptera: Culicidae).

Journal of economic entomology·2013
Same author

Study on the growth and the photosynthetic characteristics of low energy C(+) ion implantation on peanut.

PloS one·2013
Same journal

Aggregating global-scale pixel-wise forgery cues within a graph.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

FedCAD: Cross-modal semantic alignment and distillation for cross-domain heterogeneous federated learning.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Partial-encryption-decryption-based secure state estimation of singularly perturbed complex networks: A Paillier encryption approach.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

ResVaRe: Parameter-efficient fine-tuning for large language models via cross-layer residual vector adaptation and representation editing.

Neural networks : the official journal of the International Neural Network Society·2026
Same journal

Brain network construction and analysis for epilepsy: A methodology review.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Data smoothing regularization offers a simpler alternative to Tikhonov regularization for parameter learning. This method enhances algorithms like EM and supports multi-object learning with various geometrical structures.

Area of Science:

  • Computational intelligence
  • Machine learning
  • Pattern recognition

Background:

  • Introduces data smoothing regularization, a method for parameter learning.
  • Addresses challenges in hyper-parameter selection inherent in Tikhonov regularization.
  • Draws parallels to brain-like computing and intelligent information systems.

Purpose of the Study:

  • To demonstrate the application and benefits of data smoothing regularization.
  • To extend regularization techniques to multi-sets learning and probabilistic formulations.
  • To provide insights into general problem-solving strategies in machine learning.

Main Methods:

  • Application of data smoothing regularization to Gaussian-mixture models.
  • Utilized smoothed versions of the Expectation-Maximization (EM) algorithm.

Related Experiment Videos

  • Extended to a probabilistic formulation for multi-sets learning of geometrical structures.

    • Main Results:

    • Demonstrated the effectiveness of data smoothing regularization on various algorithms.
    • Enabled learning of multiple objects with geometrical structures (points, lines, circles, etc.).
    • Provided a new probabilistic framework for multi-sets learning.

    Conclusions:

    • Data smoothing regularization offers a practical approach to parameter learning.
    • The method facilitates advanced machine learning tasks, including multi-object recognition.
    • Suggested a general problem-solving paradigm integrating competition, evidence accumulation, and guess-test strategies.