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Related Concept Videos

Multiple Regression01:25

Multiple Regression

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Multiple regression assesses a linear relationship between one response or dependent variable and two or more independent variables. It has many practical applications.
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Regression Toward the Mean01:52

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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...
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Regression analysis is a statistical tool that describes a mathematical relationship between a dependent variable and one or more independent variables.
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Residuals and Least-Squares Property01:11

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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
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Correlation and Regression00:53

Correlation and Regression

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Related Experiment Videos

Bidirectional extreme learning machine for regression problem and its learning effectiveness.

Yimin Yang, Yaonan Wang, Xiaofang Yuan

    IEEE Transactions on Neural Networks and Learning Systems
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    A new bidirectional extreme learning machine (B-ELM) algorithm enhances neural network training speed. This method reduces hidden nodes without sacrificing learning effectiveness, outperforming incremental ELM algorithms.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Neural Networks

    Background:

    • Traditional neural networks face limitations in learning speed and effectiveness, hindering applications.
    • Extreme Learning Machine (ELM) offers a significant speed improvement over conventional methods, reducing training time drastically.
    • A key challenge in ELM research is minimizing hidden nodes without compromising performance.

    Purpose of the Study:

    • To introduce a novel learning algorithm, the bidirectional extreme learning machine (B-ELM).
    • To investigate the potential for reducing hidden nodes in ELM while maintaining learning effectiveness.
    • To explore the relationship between network output error and output weights in B-ELM.

    Main Methods:

    • Development of the bidirectional extreme learning machine (B-ELM) algorithm.
    • Implementation of a strategy where hidden nodes are not exclusively randomly selected.
    • Theoretical analysis of B-ELM's convergence properties and error reduction.

    Main Results:

    • B-ELM demonstrates a tendency to achieve near-zero network output error at early learning stages.
    • A direct relationship between network output error and output weights was identified within B-ELM.
    • Simulations show B-ELM is significantly faster (tens to hundreds of times) than incremental ELM algorithms.

    Conclusions:

    • The bidirectional extreme learning machine (B-ELM) offers a more efficient approach to neural network training.
    • B-ELM effectively reduces training time and potentially the number of hidden nodes without performance degradation.
    • This algorithm presents a promising advancement for applications requiring rapid and effective neural network learning.