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

Multiple Regression01:25

Multiple Regression

3.7K
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...
3.7K
Residuals and Least-Squares Property01:11

Residuals and Least-Squares Property

8.8K
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...
8.8K
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

325
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 of...
325
Regression Toward the Mean01:52

Regression Toward the Mean

6.8K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.8K
Response Surface Methodology01:16

Response Surface Methodology

514
Response Surface Methodology (RSM) is a collection of statistical and mathematical techniques used to develop, improve, and optimize processes. It is particularly valuable when many input variables or factors potentially influence a response variable.
The process of RSM involves several key steps:
514
Regression Analysis01:11

Regression Analysis

7.6K
Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
In regression analysis, a regression equation is determined based on the line of best fit– a line that best fits the data points plotted in a graph. This line is also called the regression line. The algebraic equation for the regression line is called the regression equation. It is represented as:
7.6K

You might also read

Related Articles

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

Sort by
Same author

LS-SVR as a Bayesian RBF Network.

IEEE transactions on neural networks and learning systems·2019
See all related articles

Related Experiment Video

Updated: Dec 22, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.2K

Parsimonious Minimal Learning Machine via Multiresponse Sparse Regression.

Madson L D Dias1, Átilla N Maia2, Ajalmar R da Rocha Neto2

  • 1Department of Computer Science, Federal University of Ceará, Fortaleza, Ceará 60355-636, Brazil.

International Journal of Neural Systems
|May 9, 2020
PubMed
Summary
This summary is machine-generated.

This study introduces an optimal selection method for minimal learning machine (MLM) input reference points (IRP) using regression and cross-validation. The new approach yields sparser models and competitive performance in classification tasks.

Keywords:
Minimal learning machinemultiresponse sparse regressionreference points

More Related Videos

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Related Experiment Videos

Last Updated: Dec 22, 2025

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions
07:34

A Simple Stimulatory Device for Evoking Point-like Tactile Stimuli: A Searchlight for LFP to Spike Transitions

Published on: March 25, 2014

10.2K
Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.6K

Area of Science:

  • Machine Learning
  • Pattern Recognition

Background:

  • The standard Minimal Learning Machine (MLM) training requires selecting input reference points (IRP) and output reference points (ORP) randomly.
  • This random selection can lead to complex models with suboptimal decision function smoothness.

Purpose of the Study:

  • To introduce a novel method for optimally selecting IRP in MLMs for classification.
  • To improve model sparsity and performance compared to traditional random selection.

Main Methods:

  • The proposed Optimally Selected Minimal Learning Machine (OS-MLM) utilizes multiresponse sparse regression (MRSR) for pattern relevance ranking.
  • A leave-one-out (LOO) cross-validation criterion is employed to determine the optimal number of IRP.

Main Results:

  • The OS-MLM approach successfully produced sparser models.
  • Experimental results on UCI datasets demonstrated competitive performance against the standard MLM strategy.

Conclusions:

  • The OS-MLM method offers an effective way to select IRP, enhancing model efficiency and performance in classification tasks.
  • This approach addresses limitations of random IRP selection in conventional MLMs.