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Related Concept Videos

Associative Learning01:27

Associative Learning

637
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...
637
Avoidance Learning and Learned Helplessness01:14

Avoidance Learning and Learned Helplessness

1.9K
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...
1.9K
Introduction to Learning01:18

Introduction to Learning

588
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...
588
Observational Learning01:12

Observational Learning

360
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...
360
Purposive Learning01:22

Purposive Learning

215
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
215
Cognitive Learning01:21

Cognitive Learning

693
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
693

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Related Experiment Videos

Adversarial Learning With Cost-Sensitive Classes.

Haojing Shen, Sihong Chen, Ran Wang

    IEEE Transactions on Cybernetics
    |February 11, 2022
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel framework for adversarial learning, enhancing protection for special classes against attacks. The proposed model improves adversarial robustness and outperforms existing methods when under attack, especially for protected classes.

    Related Experiment Videos

    Area of Science:

    • Artificial Intelligence
    • Machine Learning
    • Computer Vision

    Background:

    • Adversarial learning requires improved performance and protection for specific data classes against attacks.
    • Existing methods struggle to adequately defend against adversarial examples, particularly for sensitive classes.

    Purpose of the Study:

    • To propose a framework combining cost-sensitive classification and adversarial learning to enhance model robustness.
    • To develop a defense model against adversarial examples by leveraging the observed Min-Max property.

    Main Methods:

    • A novel framework integrating cost-sensitive classification with adversarial learning was developed.
    • The Min-Max property observed in deep neural network training was formulated and analyzed.
    • A new defense model was constructed based on the Min-Max property for improved adversarial robustness.

    Main Results:

    • The proposed model demonstrates improved performance and robustness against adversarial attacks, especially for protected classes.
    • Experimental results show the model matches existing methods in normal conditions but surpasses them during attacks.
    • The Min-Max property was identified and analyzed, contributing to the development of the defense model.

    Conclusions:

    • The proposed framework effectively enhances adversarial robustness by protecting special classes.
    • The novel defense model, informed by the Min-Max property, offers superior protection against adversarial examples.
    • This approach represents a significant advancement in securing machine learning models against sophisticated attacks.