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Related Concept Videos

Observational Learning01:12

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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 because...
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Related Experiment Video

Updated: May 30, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

An RSVM based two-teachers-one-student semi-supervised learning algorithm.

Chien-Chung Chang1, Hsing-Kuo Pao, Yuh-Jye Lee

  • 1Department of Computer Science and Information Engineering, National Taiwan University of Science and Technology, Taipei, 10607, Taiwan. D9115009@mail.ntust.edu.tw

Neural Networks : the Official Journal of the International Neural Network Society
|August 2, 2011
PubMed
Summary
This summary is machine-generated.

We introduce a novel semi-supervised learning algorithm, two-teachers-one-student (2T1S), utilizing reduced support vector machines (RSVM). This method achieves high accuracy, even outperforming methods using all available labeled data.

Related Experiment Videos

Last Updated: May 30, 2026

Artificial Intelligence-Based System for Detecting Attention Levels in Students
06:37

Artificial Intelligence-Based System for Detecting Attention Levels in Students

Published on: December 15, 2023

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Data Science

Background:

  • Semi-supervised learning (SSL) leverages both labeled and unlabeled data.
  • Traditional multi-view methods often define views in the input space.
  • Reduced Support Vector Machines (RSVM) offer a novel approach to feature space representation.

Purpose of the Study:

  • To propose a novel multi-view algorithm for semi-supervised learning.
  • To integrate Reduced Support Vector Machines (RSVM) into a semi-supervised learning framework.
  • To develop an algorithm that effectively utilizes unlabeled data through co-training and consensus training.

Main Methods:

  • The proposed algorithm, two-teachers-one-student (2T1S), is based on RSVM.
  • RSVM defines different views in the kernel feature space, not the input space.
  • The algorithm combines co-training, where classifiers teach each other, and consensus training for labeling unlabeled data.

Main Results:

  • The 2T1S algorithm achieves high cross-validation accuracy.
  • Performance is competitive even when compared to training with complete labeled data.
  • RSVM enables SSL application by not requiring label information for reduced set selection.

Conclusions:

  • The 2T1S algorithm is an effective semi-supervised learning method.
  • Utilizing views in the kernel feature space via RSVM is a viable approach for SSL.
  • The blend of co-training and consensus training enhances the utilization of unlabeled data.