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

Purposive Learning01:22

Purposive Learning

E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a bonus...
Fischer Projections02:18

Fischer Projections

Learning to draw Fischer projections of molecules and understanding their relevance plays a crucial role in the visual depiction of organic molecules. A Fischer projection is a two-dimensional projection on a planar surface to simplify the three-dimensional wedge–dash representation of molecules. This is especially helpful in the case of molecules with multiple chiral centers that can be difficult to draw. Here, all the bonds of interest are represented as horizontal or vertical lines. While...
Polar Coordinates: Problem Solving01:27

Polar Coordinates: Problem Solving

Directional radiation patterns are central to antenna analysis, as they illustrate how signal strength varies with direction. These patterns are often modeled using polar plots, where the radial distance from the origin represents signal intensity at a given angle. A commonly used idealized form is the four-lobed rose curve, which captures the concept of directional beams in a simplified mathematical form.The four-lobed rose curve, described by r = cos⁡(2θ), features four symmetric lobes, each...
Observational Learning01:12

Observational Learning

Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning because...
Lagrange Multipliers: Problem Solving01:30

Lagrange Multipliers: Problem Solving

A silo with a cylindrical base, flat bottom, and hemispherical roof is a common design in agricultural and industrial storage due to its structural efficiency and ease of construction. Optimizing its dimensions to maximize storage capacity for a given amount of material—i.e., a fixed surface area—is a classic problem in applied calculus and engineering design. The key parameters are the radius r of the base and the height h of the cylindrical section.The total volume of the silo is obtained by...
Maximizing the Directional Derivative01:25

Maximizing the Directional Derivative

The directional derivative is a central concept in multivariable calculus that describes how a function changes at a given point when moving in a specified direction. This direction is represented by a unit vector, ensuring that only the orientation influences the rate of change. By varying the direction, different rates of change can be observed, demonstrating that the directional derivative depends strongly on the chosen direction.The directional derivative is computed using the gradient...

You might also read

Related Articles

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

Sort by
Same author

Interpersonal interactions for haptic guidance during balance exercises.

Gait & posture·2018
Same author

Functionally and phenotypically mature mouse CD8+ T cells develop in porcine thymus grafts in mice.

Xenotransplantation·1998
Same author

[The dendritic cell differentiation and antigen-presenting function of the erythroleukemia cells induced by GM-CSF].

Zhonghua yi xue za zhi·1998
Same author

Role of variable regions A and B in receptor binding domain of amphotropic murine leukemia virus envelope protein.

Journal of virology·1998
Same author

Granulocyte-macrophage colony-stimulating factor induces the differentiation of murine erythroleukaemia cells into dendritic cells.

Immunology·1998
Same author

Fission yeast expression vectors adapted for positive identification of gene insertion and green fluorescent protein fusion.

BioTechniques·1998
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

Related Experiment Video

Updated: Jul 7, 2026

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

Implementing projection pursuit learning.

Y Zhao1, C G Atkeson

  • 1Artificial Intelligence Lab., MIT, Cambridge, MA.

IEEE Transactions on Neural Networks
|January 1, 1996
PubMed
Summary
This summary is machine-generated.

We introduce parametric projection pursuit regression (PPR) for faster, more accurate machine learning. This method improves neural network performance, demonstrated by learning robot arm dynamics.

More Related Videos

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Related Experiment Videos

Last Updated: Jul 7, 2026

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents
06:25

A Real-Time Interactive System for Studying Confrontational Pursuit Behavior in Rodents

Published on: May 16, 2025

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
07:05

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Area of Science:

  • Machine Learning
  • Neural Networks
  • Statistical Modeling

Background:

  • Projection Pursuit Regression (PPR) is a statistical method for finding interesting patterns in high-dimensional data.
  • Traditional nonparametric PPR can be computationally intensive and slow to train.
  • Understanding the structure of projection pursuit learning networks is crucial for their effective application.

Purpose of the Study:

  • To propose and evaluate a parametric version of Projection Pursuit Regression (PPR) for machine learning applications.
  • To enhance training speed and predictive accuracy compared to nonparametric PPR.
  • To investigate the benefits of grouping hidden units in projection pursuit learning networks.

Main Methods:

  • Developed a parametric Projection Pursuit Regression (PPR) model with direct training.
  • Analyzed heuristics for selecting optimal initial projection directions.
  • Compared a projection pursuit learning network against a standard single hidden-layer sigmoidal neural network.

Main Results:

  • The parametric PPR demonstrated superior training speed and accuracy over nonparametric approaches.
  • Heuristic analysis provided insights into selecting effective initial projection directions.
  • Grouping hidden units in projection pursuit networks was shown to be advantageous.

Conclusions:

  • Parametric PPR offers a more efficient and accurate alternative for machine learning tasks.
  • The findings provide practical guidance for implementing and optimizing projection pursuit learning networks.
  • The study validates the utility of projection pursuit learning networks in complex problems like robot arm inverse dynamics.