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Related Concept Videos

Collisions in Multiple Dimensions: Problem Solving01:06

Collisions in Multiple Dimensions: Problem Solving

In multiple dimensions, the conservation of momentum applies in each direction independently. Hence, to solve collisions in multiple dimensions, we should write down the momentum conservation in each direction separately. To help understand collisions in multiple dimensions, consider an example.
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Survival Tree01:19

Survival Tree

Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
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Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
Collisions in Multiple Dimensions: Introduction01:05

Collisions in Multiple Dimensions: Introduction

It is far more common for collisions to occur in two dimensions; that is, the initial velocity vectors are neither parallel nor antiparallel to each other. Let's see what complications arise from this. The first idea is that momentum is a vector. Like all vectors, it can be expressed as a sum of perpendicular components (usually, though not always, an x-component and a y-component, and a z-component if necessary). Thus, when the statement of conservation of momentum is written for a problem,...
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
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Elastic Collisions: Case Study01:15

Elastic Collisions: Case Study

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Related Experiment Video

Updated: Jun 22, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Managing category proliferation in fuzzy ARTMAP caused by overlapping classes.

Wing Yee Sit1, Lee Onn Mak, Gee Wah Ng

  • 1Centre for Computational Science and Engineering, National University of Singapore, Singapore 117546, Singapore. wingyee.sit@gmail.com

IEEE Transactions on Neural Networks
|June 9, 2009
PubMed
Summary

Overlapping classes in fuzzy ARTMAP (FAM) cause issues. This study modified FAM to allow multiple class predictions and reduce categories, improving accuracy without major architectural changes.

Related Experiment Videos

Last Updated: Jun 22, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Pattern Recognition

Background:

  • Overlapping classes in fuzzy ARTMAP (FAM) lead to category proliferation and classification challenges.
  • Data belonging to multiple classes complicates accurate prediction and increases computational load.

Purpose of the Study:

  • To address the difficulties posed by overlapping classes in fuzzy ARTMAP (FAM).
  • To modify the FAM algorithm to allow multi-class predictions and reduce category proliferation.
  • To enhance predictive accuracy and efficiency in FAM with overlapping data.

Main Methods:

  • Proposed modifications to the FAM architecture to enable prediction of multiple classes.
  • Explored several modifications to mitigate the excessive creation of small categories.
  • Implemented changes without requiring significant alterations to the core FAM architecture.

Main Results:

  • Successfully suppressed the excessive creation of small categories.
  • Achieved improved predictive accuracy despite a significant reduction in the number of categories.
  • Demonstrated that FAM can be effectively adapted for overlapping class data.

Conclusions:

  • The proposed modifications offer an effective solution for handling overlapping classes in FAM.
  • The enhanced FAM maintains its core architecture while improving performance on complex datasets.
  • This research contributes to more robust and accurate fuzzy ARTMAP implementations.