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Related Concept Videos

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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Associative Learning01:27

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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

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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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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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Generalization, Discrimination, and Extinction01:24

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Generalization, discrimination, and extinction are key concepts in operant conditioning that influence how behaviors are learned and maintained.
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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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Related Experiment Video

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Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms
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Universal Target Learning: An Efficient and Effective Technique for Semi-Naive Bayesian Learning.

Siqi Gao1,2, Hua Lou3, Limin Wang2,4

  • 1College of Software, Jilin University, Changchun 130012, China.

Entropy (Basel, Switzerland)
|December 3, 2020
PubMed
Summary
This summary is machine-generated.

Universal Target Learning (UTL) enhances Bayesian Network Classifiers by identifying redundant correlations to improve generalization performance. This method mines dependencies in unlabeled data, reducing classification bias from overfitting.

Keywords:
Bayesian network classifierinformation theoryuniversal target learning

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Mining

Background:

  • Overfitting in machine learning models leads to classification bias.
  • Semi-naive Bayesian techniques aim to uncover dependencies in unlabeled data to mitigate this bias.
  • Target Learning (TL) introduces instance-specific Bayesian Network Classifiers (BNC P) complementary to those learned from training data (BNC T).

Purpose of the Study:

  • To extend Target Learning (TL) to Universal Target Learning (UTL).
  • To identify and leverage redundant correlations between attribute values.
  • To maximize the encoded information in Bayesian networks via log-likelihood.

Main Methods:

  • Redefining information theory criteria.
  • Developing Universal Target Learning (UTL) to analyze unlabeled instances.
  • Investigating the impact of UTL on k-dependence Bayesian classifiers (BNC P and BNC T).

Main Results:

  • UTL effectively identifies redundant correlations in attribute values.
  • The proposed UTL approach enhances the log-likelihood of Bayesian networks.
  • Extensive experiments on 40 UCI datasets demonstrate performance improvements.

Conclusions:

  • Universal Target Learning (UTL) is a valuable extension of Target Learning (TL).
  • UTL improves the generalization performance of Bayesian Network Classifiers (BNC).
  • The method offers a robust approach to mitigating classification bias and enhancing model accuracy.