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

Learning in brains and machines.

T Poggio1, C R Shelton

  • 1Center for Biological and Computational Learning, Massachusetts Institute of Technology, Cambridge 02139, USA.

Spatial Vision
|February 24, 2001
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

An analysis of training and generalization errors in shallow and deep networks.

Neural networks : the official journal of the International Neural Network Society·2019
Same author

Evaluation of surveillance methods for monitoring house fly abundance and activity on large commercial dairy operations.

Journal of economic entomology·2011
Same author

Multiclass cancer diagnosis using tumor gene expression signatures.

Proceedings of the National Academy of Sciences of the United States of America·2001
Same author

Categorical representation of visual stimuli in the primate prefrontal cortex.

Science (New York, N.Y.)·2001
Same author

Models of object recognition.

Nature neuroscience·2000
Same author

Hierarchical models of object recognition in cortex.

Nature neuroscience·1999
Same journal

Comment on 'angle illusion on a picture's surface' by Hammad et al. (2008).

Spatial vision·2009
Same journal

Feature-based attentional modulation increases with stimulus separation in divided-attention tasks.

Spatial vision·2009
Same journal

Spatial distance between target and irrelevant patch modulates detection in a texture segmentation task.

Spatial vision·2009
Same journal

Inhibition related impairments of coherent motion perception in the attention-induced motion blindness paradigm.

Spatial vision·2009
Same journal

Recognition units in reading: backward masking experiments.

Spatial vision·2009
Same journal

Spatial-temporal modeling of interactive image interpretation.

Spatial vision·2009
See all related articles

This research explores supervised learning, a key aspect of artificial and biological intelligence. It covers theoretical advancements, practical applications in intelligent software, and insights into the brain's learning mechanisms.

Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Machine Learning Theory

Background:

  • Learning is fundamental to both biological and artificial intelligence.
  • Understanding intelligence requires investigating learning mechanisms.

Purpose of the Study:

  • To present a decade of research in supervised learning.
  • To explore interlinked research directions: theory, applications, and neuroscience.
  • To advance the understanding of learning in intelligent systems.

Main Methods:

  • Theoretical analysis of supervised learning algorithms.
  • Development of intelligent software through engineering applications.
  • Neuroscientific investigation into the brain's learning processes.

Related Experiment Videos

Main Results:

  • Progress in supervised learning theory.
  • Successful engineering applications creating intelligent software.
  • Enhanced understanding of neural learning mechanisms.

Conclusions:

  • Supervised learning is a critical area bridging AI and neuroscience.
  • Integrated research in theory, application, and neuroscience drives progress in intelligence.
  • Further research will continue to refine intelligent systems and brain understanding.