Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Reinforcement01:23

Reinforcement

202
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
202
Reinforcement Schedules01:24

Reinforcement Schedules

144
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
144
Associative Learning01:27

Associative Learning

340
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...
340
Observational Learning01:12

Observational Learning

166
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...
166
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

106
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
106
Generalization, Discrimination, and Extinction01:24

Generalization, Discrimination, and Extinction

528
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...
528

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

A clinical evaluation of amlexanox oral adhesive pellicles in the treatment of recurrent aphthous stomatitis and comparison with amlexanox oral tablets: a randomized, placebo controlled, blinded, multicenter clinical trial.

Trials·2009
Same author

Long-term assessment of bladder and bowel dysfunction after radical hysterectomy.

Gynecologic oncology·2009
Same author

Oxidative stress contributes to silica nanoparticle-induced cytotoxicity in human embryonic kidney cells.

Toxicology in vitro : an international journal published in association with BIBRA·2009
Same author

A rapid and simple method for identifying Mycobacterium tuberculosis W-Beijing strains based on detection of a unique mutation in Rv0927c by PCR-SSCP.

Microbes and infection·2009
Same author

CO oxidation over AuPd(100) from ultrahigh vacuum to near-atmospheric pressures: the critical role of contiguous Pd atoms.

Journal of the American Chemical Society·2009
Same author

Daunorubicin-loaded magnetic nanoparticles of Fe(3)O(4) greatly enhance the responses of multidrug-resistant K562 leukemic cells in a nude mouse xenograft model to chemotherapy.

Zhongguo shi yan xue ye xue za zhi·2009
Same journal

Zero-shot reconstruction of mutant spatial transcriptomes.

Patterns (New York, N.Y.)·2026
Same journal

Dendritic nonlinearities mitigate communication costs.

Patterns (New York, N.Y.)·2026
Same journal

Erratum: Agentic AI as a coordination paradigm in digital health and agri-food systems.

Patterns (New York, N.Y.)·2026
Same journal

Spacing effect improves generalization in biological and artificial systems.

Patterns (New York, N.Y.)·2026
Same journal

A multi-modal foundation model for brain disease diagnosis and medical imaging.

Patterns (New York, N.Y.)·2026
Same journal

DuoMod-Net: Logarithmic balancing and geometric refinement for imbalanced semi-supervised medical image segmentation.

Patterns (New York, N.Y.)·2026
See all related articles

Related Experiment Video

Updated: Jun 25, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K

Unveiling value patterns via deep reinforcement learning in heterogeneous data analytics.

Yanzhi Wang1, Jianxiao Wang2,3, Feng Gao1

  • 1Department of Industrial Engineering and Management, College of Engineering, Peking University, Beijing 100871, China.

Patterns (New York, N.Y.)
|May 27, 2024
PubMed
Summary
This summary is machine-generated.

This study introduces a deep reinforcement learning approach for data valuation, improving AI efficiency by filtering low-quality data. The method enhances accuracy and efficiency in data-driven tasks, as demonstrated in wind-power prediction.

Keywords:
data governancedata valuedeep reinforcement learningenergy policyforest fire forecastingheart failure analysisincome census predictionobesity level estimationuncertainty managementwind-power prediction

More Related Videos

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Operant Learning of Drosophila at the Torque Meter
17:31

Operant Learning of Drosophila at the Torque Meter

Published on: June 16, 2008

13.6K

Related Experiment Videos

Last Updated: Jun 25, 2025

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

19.9K
A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis
05:41

A Step-by-Step Implementation of DeepBehavior, Deep Learning Toolbox for Automated Behavior Analysis

Published on: February 6, 2020

9.4K
Operant Learning of Drosophila at the Torque Meter
17:31

Operant Learning of Drosophila at the Torque Meter

Published on: June 16, 2008

13.6K

Area of Science:

  • Artificial Intelligence
  • Data Science
  • Machine Learning

Background:

  • Insufficient filtering of low-quality data hinders uncertainty management and system stability in AI applications.
  • Effective data utilization is crucial for optimizing performance across various sectors.

Purpose of the Study:

  • To introduce a novel data-valuation approach using deep reinforcement learning (DRL) to identify and leverage valuable data patterns.
  • To enhance the accuracy and efficiency of data-driven tasks by strategically filtering low-quality data.

Main Methods:

  • Employed deep reinforcement learning for iterative data valuation with strategic optimization.
  • Utilized feedback mechanisms and iterative sampling to refine data value assessment.
  • Applied the method to diverse scenarios, including wind-power prediction in China.

Main Results:

  • The DRL-based data-valuation method consistently outperformed classic methods in accuracy and efficiency.
  • Excluding 25% of low-value data in wind-power prediction improved accuracy by 10.5%.
  • The model identified 80% of linear patterns using only 42.8% of the dataset, demonstrating data's intrinsic and transferable value.

Conclusions:

  • The proposed data-valuation approach effectively enhances AI performance by prioritizing high-value data.
  • The method offers significant improvements in accuracy and efficiency, particularly in complex prediction tasks.
  • Identified a data-value-sensitive geographic belt, providing insights for policy recommendations in energy sectors.