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Merging weighted SVMs for parallel incremental learning.

Lei Zhu¹, Kazushi Ikeda², Shaoning Pang¹

¹Unitec Institute of Technology, New Zealand.

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

|February 13, 2018

Summary

This summary is machine-generated.

We introduce parallel incremental weighted Extreme Support Vector Machine (wESVM) to efficiently process large data streams. This novel approach integrates parallel and incremental learning, outperforming traditional methods in speed and scalability.

Keywords:

Extreme support vector machine (ESVM)Incremental learning Knowledge merging Parallel incremental learning Parallel learning Weighted ESVM (wESVM)

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Area of Science:

Machine Learning
Data Science
Artificial Intelligence

Background:

Processing large-scale data streams often involves separate parallel and incremental learning steps.
Existing methods can be inefficient due to sequential problem-solving.

Purpose of the Study:

To develop a unified approach for parallel incremental learning.
To introduce a novel algorithm, parallel incremental wESVM, that merges these two learning paradigms.

Main Methods:

Reformulating weighted Extreme Support Vector Machine (wESVM) for knowledge merging via matrix addition.
Applying the reformulated wESVM to data slices in an incremental learning setting.

Main Results:

The proposed parallel incremental wESVM achieves equivalence to batch wESVM in learning effectiveness.
Demonstrated significant scalability and speed advantages over batch retraining methods.

Conclusions:

Simultaneously solving parallel and incremental learning problems is feasible and effective.
Parallel incremental wESVM offers a scalable and efficient solution for processing large data streams.