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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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Cognitive Learning01:21

Cognitive Learning

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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...
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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.
Classical conditioning, also known...
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Purposive Learning01:22

Purposive Learning

174
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...
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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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Avoidance Learning and Learned Helplessness01:14

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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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Active Clustering Ensemble With Self-Paced Learning.

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    This study introduces a self-paced active clustering ensemble (SPACE) method to improve clustering by selecting uncertain data for labeling. This approach enhances ensemble accuracy by leveraging both difficult and easy data points effectively.

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    Area of Science:

    • Machine Learning
    • Data Mining
    • Artificial Intelligence

    Background:

    • Clustering ensembles combine multiple clustering results for improved performance.
    • Conventional methods can be misled by unreliable data instances lacking labels.
    • There is a need for methods that can actively select informative data for labeling within ensembles.

    Purpose of the Study:

    • To propose a novel active clustering ensemble method.
    • To integrate active learning with self-paced learning for clustering ensembles.
    • To develop a Self-Paced Active Clustering Ensemble (SPACE) method.

    Main Methods:

    • Developed a SPACE method integrating active clustering ensemble with self-paced learning.
    • Implemented a strategy to jointly select unreliable data for labeling and use easy data for ensemble.
    • Automatically evaluated data difficulty for targeted annotation.

    Main Results:

    • SPACE effectively selects uncertain and unreliable data for annotation.
    • The method leverages easy data to improve the clustering ensemble.
    • Experimental results on benchmark datasets show significant effectiveness.

    Conclusions:

    • The proposed SPACE method enhances clustering performance by intelligently selecting data for labeling.
    • Jointly optimizing data selection and ensemble learning leads to improved accuracy.
    • SPACE offers a robust framework for clustering tasks with limited labeled data.