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

Associative Learning01:27

Associative Learning

961
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...
961
Aggregates Classification01:29

Aggregates Classification

666
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
666
Observational Learning01:12

Observational Learning

699
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...
699
Introduction to Learning01:18

Introduction to Learning

753
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...
753
Classification of Signals01:30

Classification of Signals

1.2K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.2K
Classification of Systems-I01:26

Classification of Systems-I

470
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
470

You might also read

Related Articles

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

Sort by
Same author

Number Needed to Treat with Biologics in Type-2 Inflammation COPD: A Systematic Review and Meta-Analysis.

COPD·2026
Same author

Elimination of detrimental grain boundary segregation in Garnets.

Nature communications·2026
Same author

Electric mobility and the green transition: A spatial econometric perspective on global decarbonization.

Journal of environmental management·2026
Same author

Monkey upload: Improving robustness using multi-stage neural alignment.

Journal of vision·2026
Same author

Combined Chemical and Mechanical Debridement Enhances Salivary Protein Removal from Titanium while Maintaining Biological Properties.

ACS biomaterials science & engineering·2026
Same author

SMART deep learning tools to accelerate the characterization of natural product structures from their NMR data.

Methods in enzymology·2026

Related Experiment Videos

Adversarial Joint-Learning Recurrent Neural Network for Incomplete Time Series Classification.

Qianli Ma, Sen Li, Garrison W Cottrell

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 30, 2020
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces an adversarial joint-learning recurrent neural network (AJ-RNN) to address incomplete time series classification. The model effectively imputes missing data and classifies series, significantly reducing classification errors.

    Related Experiment Videos

    Area of Science:

    • Machine Learning
    • Data Science
    • Time Series Analysis

    Background:

    • Incomplete time series classification (ITSC) is crucial due to frequent missing data in real-world applications.
    • Integrating imputation and classification models often leads to amplified errors from imputed values.
    • Reducing error propagation from imputation to classification remains a significant challenge in ITSC.

    Purpose of the Study:

    • To propose an end-to-end model for ITSC that jointly learns imputation and classification.
    • To alleviate error propagation by encouraging realistic data imputation using adversarial learning.
    • To enhance classification accuracy for time series with missing values.

    Main Methods:

    • Developed an adversarial joint-learning recurrent neural network (AJ-RNN).
    • Employed an adversarial network to distinguish real from imputed values, ensuring realistic imputation.
    • Trained the model in an adversarial and joint learning manner for simultaneous imputation and classification.

    Main Results:

    • AJ-RNN achieves state-of-the-art performance on 68 synthetic and 4 real-world datasets.
    • The model effectively alleviates accumulating error problems inherent in ITSC.
    • Demonstrated significant reduction in error propagation from imputation to classification, boosting accuracy.

    Conclusions:

    • AJ-RNN offers a robust solution for ITSC by directly handling missing values.
    • The adversarial approach successfully mitigates imputation-related errors, improving classification outcomes.
    • The model's effectiveness is validated through extensive experiments and dynamical system analysis.