Optimization of support vector machine hyperparameters by using genetic algorithm

被引：0

作者：

Szymanski, Z ^{[1
]}

Jankowski, S ^{[1
]}

Grelow, D ^{[1
]}

机构：

[1] Warsaw Univ Technol, Inst Comp Sci, Nowowiejska 15-19, PL-00665 Warsaw, Poland

来源：

PHOTONICS APPLICATIONS IN ASTRONOMY, COMMUNICATIONS, INDUSTRY, AND HIGH-ENERGY PHYSICS EXPERIMENTS IV | 2006年 / 6159卷

关键词：

Support Vector Machine; genetic algorithm; hyperparameters;

D O I：

10.1117/12.674867

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

Support vector machines with Gaussian kernel are used in classification tasks with linear non-separable data. The Gaussian kernel is parametrized by two values (hyperparameters): C, gamma. Hyperparameters selection, also known as model selection, affects the generalization performance of classifier. Retaining high generalization performance is vital to achieving good prediction results on uknown datasets. There is no strict rule for proper model selection. The range of hyperparameters' values is wide, so this is a time consuming task in general. In our approach genetic algorithm is exploited to find optimal hyperparameters values.

引用

页数：6