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

Aggregates Classification01:29

Aggregates Classification

1.1K
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...
1.1K
Classification of Systems-I01:26

Classification of Systems-I

668
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:
668
Classification of Systems-II01:31

Classification of Systems-II

562
Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
562
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

482
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 of...
482
How Data are Classified: Categorical Data01:11

How Data are Classified: Categorical Data

48.1K
A variable, usually notated by capital letters such as X and Y, is a characteristic or measurement that can be determined for each member of a population. Data are the actual values of variables. They may be numbers, or they may be words. Datum is a single value.
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
48.1K
Classification of Signals01:30

Classification of Signals

1.6K
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...
1.6K

You might also read

Related Articles

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

Sort by
Same author

Still Competitive: Revisiting Recurrent Models for Irregular Time Series Prediction.

Transactions on machine learning research·2026
Same author

Hierarchical Active Learning with Label Proportions on Data Regions.

IEEE transactions on knowledge and data engineering·2025
Same author

Augmentation-Free Contrastive Learning for EKG Classification.

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )·2025
Same author

Enhancing Hypotension Prediction in Real-time Patient Monitoring Through Deep Learning: A Novel Application of XResNet with Contrastive Learning and Value Attention Mechanisms.

Artificial intelligence in medicine. Conference on Artificial Intelligence in Medicine (2005- )·2024
Same author

The influence of microbial colonization on inflammatory versus pro-healing trajectories in combat extremity wounds.

Scientific reports·2024
Same author

Personalized event prediction for Electronic Health Records.

Artificial intelligence in medicine·2023
Same journal

AnchorDrug: A system for drug-induced gene expression prediction in new contexts through active learning.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining·2026
Same journal

Domain-Adaptive Continual Meta-Learning for Modeling Dynamical Systems: An Application in Environmental Ecosystems.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining·2025
Same journal

MedDiffusion: Boosting Health Risk Prediction via Diffusion-based Data Augmentation.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining·2024
Same journal

Automated Fusion of Multimodal Electronic Health Records for Better Medical Predictions.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining·2024
Same journal

FAME: Fragment-based Conditional Molecular Generation for Phenotypic Drug Discovery.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining·2022
Same journal

Harmonic Alignment.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining·2021
See all related articles

Related Experiment Video

Updated: Mar 29, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K

A Generalized Mixture Framework for Multi-label Classification.

Charmgil Hong1, Iyad Batal2, Milos Hauskrecht1

  • 1Department of Computer Science, University of Pittsburgh.

Proceedings of the ... SIAM International Conference on Data Mining. SIAM International Conference on Data Mining
|November 28, 2015
PubMed
Summary
This summary is machine-generated.

We introduce a new probabilistic ensemble framework for multi-label classification using mixtures-of-experts. This approach effectively captures complex data relationships, outperforming current state-of-the-art methods.

Keywords:
Mixtures-of-expertsMulti-label classification

More Related Videos

Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.6K

Related Experiment Videos

Last Updated: Mar 29, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

8.1K
Cross-Modal Multivariate Pattern Analysis
13:51

Cross-Modal Multivariate Pattern Analysis

Published on: November 9, 2011

20.6K

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Science

Background:

  • Multi-label classification is challenging due to complex dependencies between labels and features.
  • Existing methods often oversimplify these relationships, limiting their performance.
  • The mixtures-of-experts architecture offers a flexible framework for modeling complex data.

Purpose of the Study:

  • To develop a novel probabilistic ensemble framework for multi-label classification.
  • To enhance the modeling of input-output and output-output dependencies in data.
  • To improve the accuracy and robustness of multi-label classification systems.

Main Methods:

  • A mixtures-of-experts architecture was employed, integrating models from the classifier chains family.
  • The framework decomposes the class posterior distribution using a product of posterior distributions.
  • Algorithms for learning the model and performing predictions on new data were developed.

Main Results:

  • The proposed framework successfully captures diverse and changing input-output and output-output relations.
  • It recovers rich dependency structures that single models cannot.
  • Experiments show highly competitive results on benchmark datasets.

Conclusions:

  • The novel probabilistic ensemble framework offers a significant advancement in multi-label classification.
  • It effectively addresses the limitations of existing methods by capturing complex dependencies.
  • The approach demonstrates superior performance compared to state-of-the-art techniques.