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Related Concept Videos

Introduction to Learning01:18

Introduction to Learning

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Learning is the process of acquiring knowledge or skills through practice or experience, leading to long-lasting behavioral changes. This acquisition occurs through interaction with the environment and requires practice or experience. For instance, mastering a skill such as surfing requires considerable practice and experience, highlighting the essential role of repeated interactions with the environment in learning.
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Machines: Problem Solving II01:30

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Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. Consider a lifting tong carrying a 100 kg load. It comprises movable sections DAF and CBG linked together with member AB.
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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.
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A toggle clamp is a mechanical device commonly used for holding and clamping objects in various applications, such as woodworking, metalworking, and assembly operations. Consider a toggle clamp subjected to a force of 200 N at the handle. The vertical clamping force can be calculated, provided the dimensions of the toggle clamp are known.
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Related Experiment Videos

Online Sequential Extreme Learning Machine With Kernels.

Simone Scardapane, Danilo Comminiello, Michele Scarpiniti

    IEEE Transactions on Neural Networks and Learning Systems
    |January 7, 2015
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces the kernel online sequential extreme learning machine (KOS-ELM), an efficient algorithm for online kernel-based learning. KOS-ELM enhances training speed and generalization error for machine learning tasks.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computational Science

    Background:

    • Extreme Learning Machine (ELM) offers a unified framework for learning algorithms.
    • Classical ELM uses batch learning with explicit or implicit (kernel) feature mappings.
    • Online learning for kernel-based ELM remains an open challenge.

    Purpose of the Study:

    • To develop an efficient online learning algorithm for kernel-based ELM.
    • To bridge the gap between nonlinear adaptive filtering and ELM theory.
    • To introduce a novel algorithm for online kernel-based ELM.

    Main Methods:

    • Proposed a straightforward extension of kernel recursive least-squares (KRLS) from kernel adaptive filtering (KAF) to ELM.
    • Developed the kernel online sequential ELM (KOS-ELM) algorithm.
    • Integrated two sparsity-inducing criteria from KAF into KOS-ELM.

    Main Results:

    • KOS-ELM demonstrates high efficiency in terms of generalization error and training time.
    • Empirical evaluations on benchmarking datasets show promising results.
    • The integrated sparsity criteria contribute to algorithm efficiency.

    Conclusions:

    • KOS-ELM provides an effective solution for online kernel-based ELM.
    • The algorithm offers a significant advancement in efficient machine learning.
    • This work opens new avenues for online learning in ELM frameworks.