Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nystr"om Approximation for Scalable Adaptive Learning

Name: Revisited Support Vector Machines: A Unified Joint Optimization Framework with Nystr"om Approximation for Scalable Adaptive Learning
Uploaded: 2026-10-18T19:59:10.147369+00:00
Duration: 260 s
Description: This work introduces Revisited SVM, a new framework that unifies optimizing dual multipliers and kernel parameters simultaneously. It tackles the cubic complexity of traditional SVMs by using Nyström approximation to reduce computational cost from O(N^3) to O(Nm). This allows for faster training and more adaptive models, achieving high accuracy in complex domains like cybersecurity.

This work introduces Revisited SVM, a new framework that unifies optimizing dual multipliers and kernel parameters simultaneously. It tackles the cubic complexity of traditional SVMs by using Nyström approximation to reduce computational cost from O(N^3) to O(Nm). This allows for faster training and more adaptive models, achieving high accuracy in complex domains like cybersecurity.

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