Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Prediction Intervals

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y.

Aggregates Classification

Aggregates Classification

Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...

Classification of Systems-II

Classification of Systems-II

Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,

Classification of Systems-I

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:

Quadratic Models

Quadratic Models

Quadratic models are mathematical representations used to describe relationships in which the rate of change changes at a constant rate. These models appear in a wide variety of natural and engineered systems, especially those involving motion, forces, and optimization. One common application is analyzing the vertical motion of objects influenced by gravity, such as a ball thrown into the air.In such scenarios, the object's height changes over time in a curved pattern, rising to a maximum point...

Interval Level of Measurement

Interval Level of Measurement

For effective statistical analysis, data are classified into four levels of measurement—nominal, ordinal, interval, and ratio.
Data measured using the interval scale are similar to ordinal level data because they have a definite arrangement. However, in the interval level of measurement, the differences between data values are meaningful even though the data does not have a starting point.
Temperature is measured using the interval scale. It is measurable data, and the difference between...

You might also read

Related Articles

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

Sort by

Same author

Automated Video-EEG Analysis in Epilepsy Studies: A Narrative Review of Advances and Challenges.

Journal of medical systems·2025

Same author

Attention-based random forest and contamination model.

Neural networks : the official journal of the International Neural Network Society·2022

Same author

SurvNAM: The machine learning survival model explanation.

Neural networks : the official journal of the International Neural Network Society·2022

Same author

Deep Forest as a framework for a new class of machine-learning models.

National science review·2021

Same author

A robust algorithm for explaining unreliable machine learning survival models using the Kolmogorov-Smirnov bounds.

Neural networks : the official journal of the International Neural Network Society·2020

Same author

Prediction of deleterious mutations in coding regions of mammals with transfer learning.

Evolutionary applications·2019

Search research articles

Related Experiment Video

Updated: Apr 9, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

A new robust model of one-class classification by interval-valued training data using the triangular kernel.

Lev V Utkin¹, Anatoly I Chekh²

¹Department of Control, Automation and System Analysis, Saint Petersburg State Forest Technical University, Russia.

Neural Networks : the Official Journal of the International Neural Network Society

|June 21, 2015

Summary

This study introduces a novel one-class classification model for interval-valued data, extending the Campbell and Bennett novelty detection method. The proposed model simplifies computations using a triangular kernel, demonstrating effective performance in experiments.

Keywords:

Extreme points Interval-valued data Kernel Linear programming Minimax strategy Novelty detection One-class classification Support vector machine

Related Experiment Videos

Last Updated: Apr 9, 2026

A Two-interval Forced-choice Task for Multisensory Comparisons

A Two-interval Forced-choice Task for Multisensory Comparisons

Published on: November 9, 2018

Area of Science:

Machine Learning
Data Mining
Pattern Recognition

Background:

Novelty detection is crucial for identifying unusual data points.
Existing methods like Campbell and Bennett's (C-B) novelty detection model face challenges with interval-valued data.
Support Vector Machines (SVMs) are powerful classification tools, but their application to interval data requires specific adaptations.

Purpose of the Study:

To develop a robust one-class classification model for interval-valued training data.
To extend the capabilities of the Campbell and Bennett (C-B) novelty detection model.
To simplify the computational complexity associated with interval-valued data analysis in novelty detection.

Main Methods:

Proposed a one-class classification model as an extension of the C-B novelty detection model.
Leveraged the property of the dual optimization problem of the C-B model to represent it as a set of simple linear programs.
Replaced the Gaussian kernel with a triangular kernel in linear Support Vector Machines (SVMs) for efficient interval data processing.

Main Results:

The proposed model simplifies the dual optimization problem into a finite set of simple linear optimization problems.
The use of a triangular kernel approximates the Gaussian kernel, enabling effective handling of interval-valued data.
Numerical experiments on both synthetic and real-world datasets demonstrated the model's strong performance.

Conclusions:

The developed one-class classification model offers an efficient and robust approach for novelty detection with interval-valued data.
The simplification of optimization problems and the use of a triangular kernel contribute to computational efficiency.
The model shows promise for practical applications requiring the analysis of uncertain or interval-based information.