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Related Concept Videos

Observational Learning01:12

Observational Learning

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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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.
In contrast to learned behaviors, unlearned behaviors such as crying, sexual...
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Uniform Depth Channel Flow: Problem Solving01:18

Uniform Depth Channel Flow: Problem Solving

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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Rapidly Varying Flow01:24

Rapidly Varying Flow

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Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
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Associative Learning01:27

Associative Learning

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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.
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Sampling Continuous Time Signal01:11

Sampling Continuous Time Signal

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In signal processing, a continuous-time signal can be sampled using an impulse-train sampling technique, followed by the zero-order hold method. Impulse-train sampling involves the use of a periodic impulse train, which consists of a series of delta functions spaced at regular intervals determined by the sampling period. When a continuous-time signal is multiplied by this impulse train, it generates impulses with amplitudes corresponding to the signal's values at the sampling points.
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Related Experiment Video

Updated: Sep 27, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
08:05

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

Published on: June 30, 2020

7.7K

Unsupervised Continual Learning in Streaming Environments.

Andri Ashfahani, Mahardhika Pratama

    IEEE Transactions on Neural Networks and Learning Systems
    |April 13, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an autonomous deep clustering network (ADCN) for data streams, enabling unsupervised feature learning and network construction. ADCN effectively clusters data on the fly without manual feature engineering or labeled samples.

    Related Experiment Videos

    Last Updated: Sep 27, 2025

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
    08:05

    Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques

    Published on: June 30, 2020

    7.7K

    Area of Science:

    • Machine Learning
    • Data Mining
    • Artificial Intelligence

    Background:

    • Deep clustering networks (DCNs) excel at feature extraction but require manual engineering.
    • Automatic DCN construction in streaming data is challenging due to high labeling costs.
    • Unsupervised approaches are increasingly demanded for data stream analysis.

    Purpose of the Study:

    • To present an unsupervised method for constructing deep clustering networks (DCNs) on the fly for data streams.
    • To introduce the autonomous deep clustering network (ADCN) that combines deep learning and clustering.
    • To address the need for automated network construction and feature extraction in unsupervised streaming environments.

    Main Methods:

    • Developed an autonomous deep clustering network (ADCN) integrating feature extraction and autonomous fully connected layers.
    • Implemented self-evolving network depth and width based on bias-variance decomposition of reconstruction loss.
    • Incorporated self-clustering in deep embedding spaces and latent-based regularization to prevent catastrophic forgetting.

    Main Results:

    • ADCN demonstrates superior performance compared to existing methods in rigorous numerical studies.
    • The network autonomously constructs its structure within streaming environments.
    • ADCN operates effectively without requiring labeled samples for model updates.

    Conclusions:

    • ADCN offers a fully autonomous solution for deep clustering network construction in streaming data.
    • The unsupervised approach bypasses the need for feature engineering and expensive data labeling.
    • The proposed method advances the field of online machine learning and deep clustering.