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

Weighted Mean00:57

Weighted Mean

7.5K
While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
7.5K
Per-Unit Sequence Models01:26

Per-Unit Sequence Models

496
An ideal Y-Y transformer, grounded through neutral impedances, displays per-unit sequence networks akin to those of a single-phase ideal transformer when subjected to balanced positive- or negative-sequence currents. These currents do not produce neutral currents, and their associated voltage drops.
Zero-sequence currents, which are identical in magnitude and phase, generate a neutral current, resulting in voltage drops across the neutral impedance and the low-voltage winding. If the...
496
Wald-Wolfowitz Runs Test I01:17

Wald-Wolfowitz Runs Test I

1.1K
The Wald-Wolfowitz test, also known as the runs test, is a nonparametric statistical test used to assess the randomness of a sequence of two different types of elements (e.g., positive/negative values, successes/failures). It examines whether the order of the elements in a sequence is random or if there is a pattern or trend present. This nonparametric test applies to any ordered data despite the population and sample data distribution, even if a higher sample size is available.
The test works...
1.1K
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

540
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
540
Woodward–Hoffmann Selection Rules and Microscopic Reversibility01:34

Woodward–Hoffmann Selection Rules and Microscopic Reversibility

4.2K
Electrocyclic reactions, cycloadditions, and sigmatropic rearrangements are concerted pericyclic reactions that proceed via a cyclic transition state. These reactions are stereospecific and regioselective. The stereochemistry of the products depends on the symmetry characteristics of the interacting orbitals and the reaction conditions. Accordingly, pericyclic reactions are classified as either symmetry-allowed or symmetry-forbidden. Woodward and Hoffmann presented the selection criteria for...
4.2K
Randomized Experiments01:13

Randomized Experiments

9.3K
The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
Simple...
9.3K

You might also read

Related Articles

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

Sort by
Same author

Distribution-Preserving Stratified Sampling for Learning Problems.

IEEE transactions on neural networks and learning systems·2017
Same author

A Novel Approach for Sampling in Approximate Dynamic Programming Based on $F$ -Discrepancy.

IEEE transactions on cybernetics·2017
Same author

An Extreme Learning Machine Approach to Density Estimation Problems.

IEEE transactions on cybernetics·2017
Same author

F -Discrepancy for Efficient Sampling in Approximate Dynamic Programming.

IEEE transactions on cybernetics·2015
Same author

Local linear regression for function learning: an analysis based on sample discrepancy.

IEEE transactions on neural networks and learning systems·2014
Same author

Learning with kernel smoothing models and low-discrepancy sampling.

IEEE transactions on neural networks and learning systems·2014

Related Experiment Videos

Low-Discrepancy Points for Deterministic Assignment of Hidden Weights in Extreme Learning Machines.

Cristiano Cervellera, Danilo Macciò

    IEEE Transactions on Neural Networks and Learning Systems
    |May 13, 2015
    PubMed
    Summary

    This study replaces random weight assignment in extreme learning machines (ELMs) with low-discrepancy sequences (LDSs). This method improves efficiency and guarantees ELM

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Computational Mathematics

    Background:

    • Traditional extreme learning machines (ELMs) utilize random hidden weight assignment.
    • Analytical methods determine output layer linear coefficients in ELMs.
    • Random assignment can lead to inefficiencies in covering multidimensional spaces.

    Purpose of the Study:

    • To investigate replacing random weight generation in ELMs with low-discrepancy sequences (LDSs).
    • To analyze the geometric properties of sampling points for ELM weight assignment.
    • To demonstrate the theoretical and practical benefits of using LDSs in ELMs.

    Main Methods:

    • Analysis of geometric properties of sampling points.
    • Replacement of random weight generation with low-discrepancy sequences (LDSs).
    • Theoretical proof of universal approximation property with LDSs.

    Main Results:

    • Universal approximation property of ELMs is guaranteed when using LDSs.
    • Efficient covering by LDSs positively impacts ELM convergence.
    • Deterministic nature of LDSs removes probabilistic outcomes.
    • Simulation results validate theoretical findings.

    Conclusions:

    • Low-discrepancy sequences offer a deterministic and efficient alternative to random assignment in ELMs.
    • The use of LDSs enhances ELM performance and theoretical guarantees.
    • This approach provides a more robust and predictable machine learning model.