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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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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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Natural and Artificial Concepts01:24

Natural and Artificial Concepts

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In psychology, concepts can be divided into two categories: natural and artificial. Natural concepts are formed through direct or indirect experiences. For example, consider the concept of snow. If you live in a place with regular snowfall, such as Essex Junction, Vermont, you know snow through direct experiences. You’ve seen it fall, touched it, shoveled it, and played in it. You recognize its texture, appearance, and even its smell. In contrast, if you live on an island like Saint...
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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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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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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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Related Experiment Video

Updated: Mar 15, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications
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Adaptive Online Sequential ELM for Concept Drift Tackling.

Arif Budiman1, Mohamad Ivan Fanany1, Chan Basaruddin1

  • 1Faculty of Computer Science, University of Indonesia, Depok, West Java 16424, Indonesia.

Computational Intelligence and Neuroscience
|September 6, 2016
PubMed
Summary

This study introduces the adaptive Online Sequential Extreme Learning Machine (AOS-ELM), a novel machine learning method designed to effectively handle concept drift in both classification and regression tasks. AOS-ELM demonstrates superior performance and adaptability to environmental changes.

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Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Machine learning models require adaptation to environmental changes, known as concept drift.
  • Existing methods like OS-ELM and CEOS-ELM have limitations in fully addressing various types of concept drift.

Purpose of the Study:

  • To propose an adaptive Online Sequential Extreme Learning Machine (AOS-ELM) for enhanced concept drift handling.
  • To develop a unified method capable of addressing real, virtual, hybrid, sudden, and recurrent concept drift in classification and regression.

Main Methods:

  • Developed the adaptive OS-ELM (AOS-ELM) as a single classifier scheme.
  • Implemented AOS-ELM with adaptive capabilities for classification and regression problems.
  • Utilized the rank of the pseudoinverse matrix as an indicator for underfitting detection.

Main Results:

  • AOS-ELM effectively handles various types of concept drift, including real, virtual, hybrid, sudden, and recurrent changes.
  • Experimental results on public datasets (SEA, STAGGER, MNIST, USPS, IDS) show higher kappa values compared to multi-classifier ELM ensembles.
  • The proposed underfitting detection mechanism using pseudoinverse matrix rank is effective.

Conclusions:

  • AOS-ELM offers a simple, unified, and effective solution for concept drift in machine learning.
  • The method provides robust performance in both classification and regression tasks.
  • The pseudoinverse matrix rank serves as a valuable parameter for monitoring model performance and detecting underfitting.