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Related Concept Videos

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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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

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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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Purposive Learning01:22

Purposive Learning

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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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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.
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Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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Efficient Exact Inference With Loss Augmented Objective in Structured Learning.

Alexander Bauer, Shinichi Nakajima, Klaus-Robert Muller

    IEEE Transactions on Neural Networks and Learning Systems
    |January 24, 2017
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    Summary
    This summary is machine-generated.

    We developed an exact inference algorithm for structural support vector machines (SVMs) that efficiently handles complex objectives. This method improves training for tasks like natural language parsing and sequence segmentation.

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    Area of Science:

    • Machine Learning
    • Artificial Intelligence
    • Computer Science

    Background:

    • Structural Support Vector Machines (SVMs) are powerful for structured output prediction.
    • Efficient inference algorithms are crucial for training state-of-the-art structural SVMs.
    • Current methods often require repeated inference for subgradient computation or finding violating configurations.

    Purpose of the Study:

    • To propose an exact inference algorithm for structural SVMs.
    • To address the challenge of maximizing nondecomposable objectives with specific high-order potentials.
    • To enable efficient loss-augmented inference for various dissimilarity measures.

    Main Methods:

    • Developed an exact inference algorithm leveraging a decomposable internal structure of high-order potentials.
    • Applied the method to loss-augmented inference for slack and margin scaling in structural SVMs.
    • Integrated computation of diverse dissimilarity measures from contingency tables.

    Main Results:

    • The proposed algorithm efficiently maximizes nondecomposable objectives.
    • Enables exact inference for a wide range of loss functions including Hamming loss, precision/recall, Fβ-loss, and intersection over union.
    • Demonstrated improved performance in natural language parsing and sequence segmentation tasks.

    Conclusions:

    • The novel exact inference algorithm enhances the applicability and efficiency of structural SVMs.
    • Facilitates more accurate and robust structured prediction models.
    • Offers significant advantages for complex tasks in natural language processing and sequence analysis.