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 Experiment Video

Updated: Jul 21, 2025

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.2K

Online Multikernel Learning Method via Online Biconvex Optimization.

Songnam Hong

    IEEE Transactions on Neural Networks and Learning Systems
    |July 28, 2023
    PubMed
    Summary
    This summary is machine-generated.

    Related Concept Videos

    One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

    One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

    573
    This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
    On...
    573
    Multi-input and Multi-variable systems01:22

    Multi-input and Multi-variable systems

    129
    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...
    129
    Reducing Line Loss01:18

    Reducing Line Loss

    173
    In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
    With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
    173
    Distributed Loads: Problem Solving01:21

    Distributed Loads: Problem Solving

    673
    Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
    673
    Introduction to Learning01:18

    Introduction to Learning

    472
    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...
    472
    Vector Algebra: Method of Components01:08

    Vector Algebra: Method of Components

    14.0K
    It is cumbersome to find the magnitudes of vectors using the parallelogram rule or using the graphical method to perform mathematical operations like addition, subtraction, and multiplication. There are two ways to circumvent this algebraic complexity. One way is to draw the vectors to scale, as in navigation, and read approximate vector lengths and angles (directions) from the graphs. The other way is to use the method of components.
    In many applications, the magnitudes and directions of...
    14.0K

    You might also read

    Related Articles

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

    Sort by
    Same author

    FedLSC: Improving Communication Efficiency and Robustness in Federated Learning With Stragglers and Adversaries.

    IEEE transactions on neural networks and learning systems·2025
    Same author

    On Practical Robust Reinforcement Learning: Adjacent Uncertainty Set and Double-Agent Algorithm.

    IEEE transactions on neural networks and learning systems·2024
    Same author

    Tighter Regret Analysis and Optimization of Online Federated Learning.

    IEEE transactions on pattern analysis and machine intelligence·2023
    Same author

    Communication-Efficient Randomized Algorithm for Multi-Kernel Online Federated Learning.

    IEEE transactions on pattern analysis and machine intelligence·2021
    Same author

    Distributed Online Learning With Multiple Kernels.

    IEEE transactions on neural networks and learning systems·2021
    Same author

    Active Learning With Multiple Kernels.

    IEEE transactions on neural networks and learning systems·2021
    Same journal

    Granular Ball-Based Noise-Resistant Fuzzy Multineighborhood Feature Selection via Label Enhancement and Feature Graph.

    IEEE transactions on neural networks and learning systems·2026
    Same journal

    Fighting Evolving Spam With ARTMAP Models: A Noise-Resilient Online Detection Framework.

    IEEE transactions on neural networks and learning systems·2026
    Same journal

    HyperSAT: Unsupervised Hypergraph Neural Networks for Weighted MaxSAT Problems.

    IEEE transactions on neural networks and learning systems·2026
    Same journal

    Negation of Basic Belief Assignment in Multisource Information Fusion on Dempster-Shafer Theory With Applications in Pattern Classification.

    IEEE transactions on neural networks and learning systems·2026
    Same journal

    Intervention Feasible Region and Driver Risk Capacity Aware Human-Machine Collaborative Safe Trajectory Planning.

    IEEE transactions on neural networks and learning systems·2026
    Same journal

    A Unified Differential Denoising Learning Framework With a Pre-Trained Model and Fuzzy Graph Networks for Drug-Drug Interaction Prediction.

    IEEE transactions on neural networks and learning systems·2026
    See all related articles

    We introduce BoKle, an online biconvex optimization (OBO) method for random feature-based online multikernel learning (RF-OMKL). BoKle offers theoretical performance guarantees, outperforming prior expert-based methods for streaming data optimization.

    Area of Science:

    • Machine Learning
    • Optimization Theory
    • Data Science

    Background:

    • Random feature-based online multikernel learning (RF-OMKL) offers low-complexity optimization for streaming data.
    • Existing methods struggle with analytical performance guarantees due to online biconvex optimization (OBO) challenges.
    • The state-of-the-art expert-based online multikernel learning (EoKle) provides asymptotic optimality for the best single kernel but is suboptimal.

    Purpose of the Study:

    • To develop an efficient RF-OMKL algorithm with analytical performance guarantees.
    • To address the suboptimality of expert-based methods by improving kernel function optimization.
    • To introduce a novel method that outperforms existing approaches for online multikernel learning.

    Main Methods:

    • Proposed collaborative expert-based online multikernel learning (CoKle) using a collaborative Hedge (CoHedge) algorithm.

    More Related Videos

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.1K
    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    1.6K

    Related Experiment Videos

    Last Updated: Jul 21, 2025

    Deep Neural Networks for Image-Based Dietary Assessment
    13:19

    Deep Neural Networks for Image-Based Dietary Assessment

    Published on: March 13, 2021

    9.2K
    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning
    12:06

    Analyzing Mitochondrial Morphology Through Simulation Supervised Learning

    Published on: March 3, 2023

    4.1K
    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping
    07:11

    Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

    Published on: December 8, 2023

    1.6K
  • Developed an online biconvex optimization (OBO)-based method, named BoKle, for RF-OMKL.
  • Provided partial theoretical proof of asymptotic optimality for BoKle.

    • Main Results:

    • CoKle achieves asymptotic optimality for the best combination of optimal kernel functions, offering the first theoretical guarantee for expert-based RF-OMKL.
    • BoKle demonstrates superior performance compared to expert-based methods like CoKle and EoKle.
    • Experimental results on real datasets validate the effectiveness and superiority of BoKle.

    Conclusions:

    • BoKle represents a significant advancement in RF-OMKL, providing theoretical guarantees and improved performance.
    • The OBO-based approach in BoKle overcomes limitations of previous expert-based methods.
    • BoKle is a promising solution for machine learning optimization with continuous streaming data.