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Classification of Systems-I

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Related Experiment Video

Updated: Mar 21, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Distributed semi-supervised support vector machines.

Simone Scardapane¹, Roberto Fierimonte¹, Paolo Di Lorenzo²

¹Department of Information Engineering, Electronics and Telecommunications (DIET), "Sapienza" University of Rome, Via Eudossiana 18, 00184 Rome, Italy.

Neural Networks : the Official Journal of the International Neural Network Society

|May 16, 2016

Summary

This summary is machine-generated.

This study introduces distributed training for semi-supervised support vector machines (S3VMs) across networks. The novel methods achieve performance comparable to centralized approaches, enabling decentralized machine learning.

Keywords:

Distributed learning Networks Semi-supervised learning Support vector machine

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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Related Experiment Videos

Last Updated: Mar 21, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

Published on: October 27, 2016

Area of Science:

Machine Learning
Distributed Systems
Optimization

Background:

Semi-supervised support vector machines (S3VMs) are effective for large margin inference.
Training S3VMs typically requires centralized data access.
Decentralized training is challenging due to network constraints and lack of a central authority.

Purpose of the Study:

To develop a distributed training protocol for S3VMs over networks.
To enable S3VM training with only neighbor-to-neighbor communication.
To address the challenge of decentralized semi-supervised learning.

Main Methods:

Formulated S3VM training as distributed non-convex social cost function minimization.
Employed distributed gradient descent for optimization.
Utilized the In-Network Nonconvex Optimization (NEXT) framework with successive convexifications.

Main Results:

Proposed distributed algorithms achieve performance comparable to centralized S3VM training.
Experimental results demonstrate the feasibility of decentralized S3VM training.
Identified the advantages and disadvantages of the proposed distributed strategies.

Conclusions:

This work presents the first approach to distributed semi-supervised learning over networks.
The developed protocols facilitate S3VM training in decentralized environments.
The findings open avenues for broader applications of distributed machine learning.