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Fast projected gradient method for support vector machines

Bloom, Veronica / Griva, Igor / Quijada, Fabio

Abstract We present an algorithm for training dual soft margin support vector machines (SVMs) based on an augmented Lagrangian (AL) that uses a modification of the fast projected gradient method (FPGM) with a projection on a box set. The FPGM requires only first derivatives, which for the dual soft margin SVM means computing mainly a matrix-vector product. Therefore, AL-FPGM being computationally inexpensive may complement existing quadratic programming solvers for training large SVMs. We report numerical results for training the SVM with the AL-FPGM on data up to tens of thousands of data points from the UC Irvine Machine Learning Repository.

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Fast projected gradient method for support vector machines

Bloom, Veronica / Griva, Igor / Quijada, Fabio

Abstract We present an algorithm for training dual soft margin support vector machines (SVMs) based on an augmented Lagrangian (AL) that uses a modification of the fast projected gradient method (FPGM) with a projection on a box set. The FPGM requires only first derivatives, which for the dual soft margin SVM means computing mainly a matrix-vector product. Therefore, AL-FPGM being computationally inexpensive may complement existing quadratic programming solvers for training large SVMs. We report numerical results for training the SVM with the AL-FPGM on data up to tens of thousands of data points from the UC Irvine Machine Learning Repository.

Fast projected gradient method for support vector machines

Bloom, Veronica / Griva, Igor / Quijada, Fabio

Abstract We present an algorithm for training dual soft margin support vector machines (SVMs) based on an augmented Lagrangian (AL) that uses a modification of the fast projected gradient method (FPGM) with a projection on a box set. The FPGM requires only first derivatives, which for the dual soft margin SVM means computing mainly a matrix-vector product. Therefore, AL-FPGM being computationally inexpensive may complement existing quadratic programming solvers for training large SVMs. We report numerical results for training the SVM with the AL-FPGM on data up to tens of thousands of data points from the UC Irvine Machine Learning Repository.

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Title:

Fast projected gradient method for support vector machines

Contributors:

Bloom, Veronica (author) / Griva, Igor (author) / Quijada, Fabio (author)

Published in:

Optimization and Engineering ; 17 ; 651-662

Publication date:

2016-08-11

Size:

12 pages

ISSN:

1573-2924 , 1389-4420

DOI:

https://doi.org/10.1007/s11081-016-9328-z

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

English

Keywords:

Machine learning , Optimization , Support vector machines , Fast gradient Mathematics , Optimization , Engineering, general , Systems Theory, Control , Environmental Management , Operation Research/Decision Theory , Financial Engineering

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