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Related Concept Videos

Associative Learning01:27

Associative Learning

1.2K
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.
Classical conditioning, also known...
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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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Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
Generalization occurs when a behavior reinforced in one context is performed in similar situations. For instance, a student who studies diligently for calculus and receives excellent grades might apply the same study habits to psychology and history, expecting similar results. Generalization shows how learning in one setting can influence behavior in...
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Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

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Avoidance learning and learned helplessness are critical concepts in understanding behavioral responses to negative stimuli.
Avoidance learning occurs when an organism learns that a specific behavior can prevent an unpleasant outcome. For example, a student who receives a bad grade may start studying harder to avoid future poor grades. This behavior persists even when the negative outcome is no longer present. Avoidance learning is powerful because it maintains behavior in the absence of the...
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Distance Corrections01:15

Distance Corrections

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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Related Experiment Video

Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

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Robust Deep Active Learning via Distance-Measured Data Mixing and Adversarial Training.

Shinan Song1, Xing Wang1, Shike Dong2

  • 1School of Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China.

Entropy (Basel, Switzerland)
|November 26, 2025
PubMed
Summary

This study introduces Distance-Measured Data Mixing (DM2), a new active learning framework. DM2 improves data selection by capturing inter-sample relationships, leading to more efficient learning with fewer labeled samples.

Keywords:
active learningdata selectionrobustnessuncertainty estimation

Related Experiment Videos

Last Updated: Jan 10, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
03:14

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

1000

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Vision

Background:

  • Accurate uncertainty estimation in unlabeled data is crucial for active learning.
  • Traditional deep active learning methods struggle with noisy boundaries and suboptimal data selection.
  • Existing uncertainty and diversity-based strategies have limitations in identifying informative samples.

Purpose of the Study:

  • To develop a novel framework for improved uncertainty estimation and sample selection in active learning.
  • To address the limitations of traditional methods in handling noisy decision boundaries and diverse data distributions.
  • To enhance model robustness and generalization in complex or imbalanced datasets.

Main Methods:

  • Introduced Distance-Measured Data Mixing (DM2) for uncertainty estimation via distance-weighted data mixing.
  • Captured inter-sample relationships and data manifold structure for informative sample selection.
  • Integrated a boundary-aware feature fusion mechanism with fast gradient adversarial training to enhance robustness.

Main Results:

  • DM2 enables informative sample selection across the entire data distribution.
  • The framework effectively balances focus on near-boundary regions without overfitting to ambiguous instances.
  • Demonstrated consistent outperformance over strong uncertainty-based and diversity-based baselines across diverse tasks and data modalities.

Conclusions:

  • DM2 offers a superior approach to active learning sample selection compared to existing methods.
  • The proposed framework significantly reduces the number of labeled samples required for effective model training.
  • DM2 enhances model robustness and generalization, particularly under complex or imbalanced data conditions.