Jove
Visualize
Contact Us

Related Concept Videos

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
93
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

368
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
368
Associative Learning01:27

Associative Learning

283
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
283
Reducing Line Loss01:18

Reducing Line Loss

141
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
141
Multiple Regression01:25

Multiple Regression

2.9K
Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
Farmers can use multiple regression to determine the crop yield based on more than one factor, such as water availability, fertilizer, soil properties, etc. Here, the crop yield is the response or dependent variable as it depends on the other independent variables. The analysis requires the construction of a scatter plot...
2.9K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

7.3K
The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
If the observed data point lies above the line, the residual is positive, and the line underestimates the actual data value for y. If the observed data point lies below the line, the residual is negative, and the line overestimates the actual data value for y.
The process of fitting the best-fit...
7.3K

You might also read

Related Articles

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

Sort by
Same journal

Multimodal imaging to analyze the biomechanical properties of kidney tumors, evaluating feasibility, inter-modality correspondence, and diagnostic value (UroCCR-115).

PloS one·2026
Same journal

Effect of Cosmos Caudatus supplementation and aerobic exercise on selected neurobehaviour, biochemical profile and histology in rats with mild cognitive impairment (MCI) induced by AlCl3: Study Protocol.

PloS one·2026
Same journal

Towards new animal models of pure hypoxic Lance-Adams syndrome: Negative results.

PloS one·2026
Same journal

Dynamic changes in In vitro rumen fermentation, nutrient degradation, and microbial communities of fermentation inoculant-treated licorice stem and leaf silage under different dry matter contents.

PloS one·2026
Same journal

Creep damage model of rock considering the influence of fractional order and temperature.

PloS one·2026
Same journal

Relationship between appearance-related social media consciousness and beliefs about obese persons among physical education teacher candidates.

PloS one·2026
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 Video

Updated: May 28, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

941

Cost-sensitive multi-kernel ELM based on reduced expectation kernel auto-encoder.

Liang Yixuan1,2

  • 1School of Science, Xi 'an University of Technology, Xi'an, Shaanxi, P. R. China.

Plos One
|February 13, 2025
PubMed
Summary
This summary is machine-generated.

This study introduces a novel multi-kernel cost-sensitive Extreme Learning Machine (ELM) method using an expectation kernel auto-encoder. This approach aims to improve training speed and generalization performance for kernel-based ELM models.

More Related Videos

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

483

Related Experiment Videos

Last Updated: May 28, 2025

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches
09:47

Author Spotlight: Advancing Alzheimer's Research – Exploring Early Detection and Multi-Omics Approaches

Published on: December 15, 2023

941
Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation
06:09

P300-Based Brain-Computer Interface Speller Performance Estimation with Classifier-Based Latency Estimation

Published on: September 8, 2023

483

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computational Science

Background:

  • Extreme Learning Machines (ELM) offer rapid training and strong generalization.
  • Existing kernel ELM auto-encoders suffer from long training times and complex parameter tuning.
  • Multi-kernel models face challenges in setting kernel function weights.

Purpose of the Study:

  • To propose a novel multi-kernel cost-sensitive ELM method.
  • To address the limitations of existing kernel ELM auto-encoders and multi-kernel approaches.
  • To enhance training efficiency and generalization performance in ELM models.

Main Methods:

  • A reduced kernel auto-encoder is defined using random reference points for similarity.
  • A reduced expectation kernel auto-encoder is designed, combining random and similarity mapping.
  • Two multi-kernel ELM models are developed, converting classifier output to posterior probability.
  • Cost-sensitive decision-making is implemented via the minimum risk criterion.

Main Results:

  • The proposed method effectively addresses long training times and parameter setting difficulties.
  • Experimental validation on public and realistic datasets demonstrates the method's efficacy.
  • The approach successfully integrates expectation kernel ELM with multi-kernel strategies.

Conclusions:

  • The developed multi-kernel cost-sensitive ELM method offers an effective solution for kernel-based ELM.
  • The method shows significant improvements in training efficiency and generalization.
  • This work contributes to advancing kernel ELM auto-encoder methodologies.