Approximate Parameter Tuning of Support Vector Machines

2011 ◽  
Author(s):  
Shizhong Liao ◽  
Chenhao Yang ◽  
Lizhong Ding
Keyword(s):  
Support Vector Machines ◽  
Parameter Tuning ◽  
Support Vector ◽  
Vector Machines

scholarly journals Detecting hospital-acquired infections: A document classification approach using support vector machines and gradient tree boosting

2016 ◽  
Vol 24 (1) ◽  
pp. 24-42 ◽  
Author(s):  
Claudia Ehrentraut ◽  
Markus Ekholm ◽  
Hideyuki Tanushi ◽  
Jörg Tiedemann ◽  
Hercules Dalianis
Keyword(s):  
Support Vector Machines ◽  
Significant Risk ◽  
Parameter Tuning ◽  
Hospital Staff ◽  
Support Vector ◽  
Patient Records ◽  
Hospital Acquired Infections ◽  
Vector Machines ◽  
Patient Health ◽  
Hospital Acquired

Hospital-acquired infections pose a significant risk to patient health, while their surveillance is an additional workload for hospital staff. Our overall aim is to build a surveillance system that reliably detects all patient records that potentially include hospital-acquired infections. This is to reduce the burden of having the hospital staff manually check patient records. This study focuses on the application of text classification using support vector machines and gradient tree boosting to the problem. Support vector machines and gradient tree boosting have never been applied to the problem of detecting hospital-acquired infections in Swedish patient records, and according to our experiments, they lead to encouraging results. The best result is yielded by gradient tree boosting, at 93.7 percent recall, 79.7 percent precision and 85.7 percent F1 score when using stemming. We can show that simple preprocessing techniques and parameter tuning can lead to high recall (which we aim for in screening patient records) with appropriate precision for this task.

Optimizing parameters of support vector machines using team-search-based particle swarm optimization

2015 ◽  
Vol 32 (5) ◽  
pp. 1194-1213 ◽  
Author(s):  
Long Zhang ◽  
Jianhua Wang
Keyword(s):  
Particle Swarm Optimization ◽  
Support Vector Machines ◽  
Search Strategy ◽  
Cross Validation ◽  
Particle Swarm ◽  
Parameter Tuning ◽  
Support Vector ◽  
Swarm Optimization ◽  
Content Type ◽  
Vector Machines

Purpose – It is greatly important to select the parameters for support vector machines (SVM), which is usually determined by cross-validation. However, the cross-validation is very time-consuming and complicated to create good parameters for SVM. The parameter tuning issue can be solved in the optimization framework. The paper aims to discuss these issues. Design/methodology/approach – In this paper, the authors propose a novel variant of particle swarm optimization (PSO) for the selection of parameters in SVM. The proposed algorithm is denoted as PSO-TS (PSO algorithm with team-search strategy), which is with team-based local search strategy and dynamic inertia factor. The ultimate design purpose of the strategy is to realize that the algorithm can be suitable for different problems with good balance between exploration and exploitation and efficiently control the inertia of the flight. In PSO-TS, the particles accomplish the assigned tasks according to different topology and detailedly search the achieved and potential regions. The authors also theoretically analyze the behavior of PSO-TS and demonstrate they can share the different information from their neighbors to maintain diversity for efficient search. Findings – The validation of PSO-TS is conducted over a widely used benchmark functions and applied to tuning the parameters of SVM. The experimental results demonstrate that the proposed algorithm can tune the parameters of SVM efficiently. Originality/value – The developed method is original.

scholarly journals A nested heuristic for parameter tuning in Support Vector Machines

2014 ◽  
Vol 43 ◽  
pp. 328-334 ◽  
Author(s):  
Emilio Carrizosa ◽  
Belén Martín-Barragán ◽  
Dolores Romero Morales
Keyword(s):  
Support Vector Machines ◽  
Parameter Tuning ◽  
Support Vector ◽  
Vector Machines
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