### Abstract

© Springer International Publishing AG 2017. Performing predictions using a non-linear support vector machine (SVM) can be too expensive in some large-scale scenarios. In the non-linear case, the complexity of storing and using the classifier is determined by the number of support vectors, which is often a significant fraction of the training data. This is a major limitation in applications where the model needs to be evaluated many times to accomplish a task, such as those arising in computer vision and web search ranking. We propose an efficient algorithm to compute sparse approximations of a non-linear SVM, i.e., to reduce the number of support vectors in the model. The algorithm is based on the solution of a Lasso problem in the feature space induced by the kernel. Importantly, this formulation does not require access to the entire training set, can be solved very efficiently and involves significantly less parameter tuning than alternative approaches. We present experiments on well-known datasets to demonstrate our claims and make our implementation publicly available.

Original language | English |
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Title of host publication | Efficient sparse approximation of support vector machines solving a kernel Lasso |

Pages | 208-216 |

Number of pages | 9 |

ISBN (Electronic) | 9783319522760 |

DOIs | |

Publication status | Published - 1 Jan 2017 |

Event | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) - Duration: 1 Jan 2019 → … |

### Publication series

Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 10125 LNCS |

ISSN (Print) | 0302-9743 |

### Conference

Conference | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Period | 1/01/19 → … |

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## Cite this

Aliquintuy, M., Frandi, E., Ñanculef, R., & Suykens, J. A. K. (2017). Efficient sparse approximation of support vector machines solving a kernel Lasso. In

*Efficient sparse approximation of support vector machines solving a kernel Lasso*(pp. 208-216). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10125 LNCS). https://doi.org/10.1007/978-3-319-52277-7_26