Fast projected gradient method for support vector machines

作者：Bloom Veronica; Griva Igor^*; Quijada Fabio

来源：Optimization and Engineering, 2016, 17(4): 651-662.

DOI：10.1007/s11081-016-9328-z

• Machine learning
• Optimization
• Support vector machines
• Fast gradient

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.

• 出版日期2016-12