Computer Science and Engineering
 Gothenburg University | Chalmers

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Parallel Support Vector Machines

S. Tavara

2018. Under review.

The immense amount of data created by digitalization requires parallel computing for machine learning methods. While there are many parallel implementations for support vector machines, there is no clear suggestion for every application scenario. Many factors including optimization algorithm, problem size and dimension, kernel function, parallel programming stack, hardware architecture impact the efficiency of implementations. It is up to the user to balance trade-offs particularly between computation time and the classification accuracy. In this survey, we review the state of the art implementations of SVM, their pros and cons, and suggest possible avenues for future research.