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

Hints

Y S Abu-Mostafa1

  • 1California Institute of Technology, Pasadena 91125, USA.

Neural Computation
|July 1, 1995
PubMed
Summary
This summary is machine-generated.

This review explores using hints in machine learning, which combine prior knowledge with data for better predictions. Hints improve learning models, especially in noisy markets like foreign exchange.

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

Recognitive aspects of moment invariants.

IEEE transactions on pattern analysis and machine intelligence·2012
Same author

Image normalization by complex moments.

IEEE transactions on pattern analysis and machine intelligence·2011
Same author

Financial model calibration using consistency hints.

IEEE transactions on neural networks·2008
Same author

No free lunch for early stopping.

Neural computation·1999
Same author

An analog feedback associative memory.

IEEE transactions on neural networks·1993
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

Area of Science:

  • Machine Learning
  • Artificial Intelligence

Background:

  • The learning-from-examples paradigm traditionally relies on training data.
  • Integrating external knowledge, or hints, can enhance learning efficiency and accuracy.
  • Hints represent known properties of the target function, independent of specific training examples.

Purpose of the Study:

  • To systematically review the application and impact of hints in machine learning.
  • To explore how hints can be integrated with existing learning models and techniques.
  • To analyze the theoretical underpinnings of hint utility in learning.

Main Methods:

  • Hints are represented as virtual examples, treated equally with training data.
  • Objective functions and learning schedules are used to balance information from various hints.

Related Experiment Videos

  • The Adaptive Minimization algorithm is employed to relate individual hint performance to overall performance.

    • Main Results:

    • The integration of hints effectively combines rules and data in the learning process.
    • Hints are compatible with diverse learning models, optimization, and regularization techniques.
    • The approach is illustrated with applications in forecasting noisy foreign-exchange markets.

    Conclusions:

    • Hints offer a powerful mechanism to enhance machine learning by incorporating prior knowledge.
    • Balancing information from hints is crucial and achievable through adaptive algorithms.
    • The theoretical value of hints relates to information and complexity, impacting VC dimension.