The Kernelized Stochastic Batch Perceptron

TL;DR

This paper introduces a new kernel SVM training method with improved theoretical runtime guarantees and demonstrates its practical effectiveness over existing approaches.

Contribution

A novel kernel SVM training algorithm with superior theoretical guarantees and practical performance compared to prior methods.

Findings

Better learning runtime guarantees than existing methods

Effective in practice compared to alternatives

Demonstrates practical applicability of the new approach

Abstract

We present a novel approach for training kernel Support Vector Machines, establish learning runtime guarantees for our method that are better then those of any other known kernelized SVM optimization approach, and show that our method works well in practice compared to existing alternatives.