The Kernelized Stochastic Batch Perceptron
Andrew Cotter, Shai Shalev-Shwartz, Nathan Srebro

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.
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Taxonomy
TopicsNeural Networks and Applications · Machine Learning and Algorithms · Face and Expression Recognition
