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Related Concept Videos

Purposive Learning

Purposive Learning

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...

Associative Learning

Associative Learning

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...

Observational Learning

Observational Learning

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...

Cognitive Learning

Cognitive Learning

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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Classical Conditioning

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Associative learning, a core principle in behavioral psychology, involves forming connections between events and facilitating learned responses. This concept is vividly illustrated by classical conditioning, a process extensively studied by the Russian physiologist Ivan Pavlov. Pavlov's pioneering research on dogs' digestive systems led to the discovery that behaviors can be learned through association, laying the groundwork for classical conditioning.
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Surveys

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Often, psychologists develop surveys as a means of gathering data. Surveys are lists of questions to be answered by research participants, and can be delivered as paper-and-pencil questionnaires, administered electronically, or conducted verbally. Generally, the survey itself can be completed in a short time, and the ease of administering a survey makes it easy to collect data from a large number of people.

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Related Experiment Video

Updated: May 11, 2025

Defining the Role Of Language in Infants' Object Categorization with Eye-tracking Paradigms

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Published on: February 8, 2019

A recent survey on instance-dependent positive and unlabeled learning.

Chen Gong^1,2, Muhammad Imran Zulfiqar^1,3, Chuang Zhang¹

¹School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China.

Fundamental Research

|April 17, 2025

Summary

Instance-dependent Positive and Unlabeled (PU) learning assumes labeling confidence varies for positive instances. This survey reviews methods for this setting, crucial for applications like medical diagnosis and outlier detection.

Keywords:

Cost-sensitive learning Instance-dependent positive and unlabeled learning Label noise learning Labeling assumption Scoring function Weakly supervised learning

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Area of Science:

Machine Learning
Artificial Intelligence
Data Science

Background:

Positive and Unlabeled (PU) learning is a semi-supervised learning task.
Instance-dependent PU learning addresses varying labeling confidence for positive instances.
This setting is vital for real-world applications like medical diagnosis and outlier detection.

Purpose of the Study:

To provide a comprehensive overview of instance-dependent PU learning.
To review existing settings and methodologies within this domain.
To analyze and compare different instance-dependent PU learning approaches.

Main Methods:

Literature review of instance-dependent PU learning settings and methods.
Comparative analysis of representative methods on benchmark datasets.
Performance evaluation against traditional PU learning techniques.

Main Results:

Instance-dependent PU learning offers a more realistic approach to PU learning.
Comparative analysis highlights the strengths and weaknesses of various methods.
Performance insights guide the selection of appropriate algorithms for specific applications.

Conclusions:

Instance-dependent PU learning is a significant advancement in PU learning.
Further research is needed to explore novel methods and applications.
This survey provides a foundation for future advancements in the field.