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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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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.
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Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
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SNR: Symbolic network-based rectifiable learning framework for symbolic regression.

Jingyi Liu1, Weijun Li1, Lina Yu1

  • 1Institute of Semiconductors, Chinese Academy of Sciences, 100083, Beijing, China; Center of Materials Science and Optoelectronics Engineering & School of Integrated Circuits, University of Chinese Academy of Sciences, 100049, Beijing, China; Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology, 100083, Beijing, China.

Neural Networks : the Official Journal of the International Neural Network Society
|July 19, 2023
PubMed
Summary
This summary is machine-generated.

Symbolic regression (SR) methods can now be improved with a new framework (SNR) that corrects errors. This approach enhances accuracy on unseen data by using a Symbolic Network (SymNet) and a rectification mechanism.

Keywords:
Learning-from-scratchLearning-with-experienceSymbolic networkSymbolic regression

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Area of Science:

  • Machine Learning
  • Data Science
  • Computational Mathematics

Background:

  • Symbolic regression (SR) discovers mathematical expressions from data.
  • Current SR methods include learning-from-scratch and learning-with-experience.
  • Learning-with-experience is faster but struggles with unseen data and lacks error correction.

Purpose of the Study:

  • To introduce a novel Symbolic Network-based Rectifiable Learning Framework (SNR).
  • To address limitations of existing SR methods, particularly in handling unseen data distributions and error correction.
  • To improve the accuracy and applicability of symbolic regression.

Main Methods:

  • The proposed SNR framework utilizes Symbolic Networks (SymNet) to represent mathematical expressions.
  • SymNet encodings provide supervised information for training a policy net (PolicyNet).
  • A rectification mechanism is incorporated to revise incorrectly predicted expressions.

Main Results:

  • SNR achieves the highest averaged coefficient of determination on self-generated datasets.
  • The method demonstrates superior accuracy compared to state-of-the-art techniques on public datasets.
  • The rectification mechanism enhances the framework's applicability to diverse data distributions.

Conclusions:

  • The Symbolic Network-based Rectifiable Learning Framework (SNR) offers significant improvements over existing SR methods.
  • SNR's ability to correct errors and its use of PolicyNet guidance enhance predictive accuracy.
  • This framework provides a more robust and broadly applicable solution for symbolic regression tasks.