The paper presents our approach to SVM implementation in parallel environment. We describe how classification learning and prediction phases were pararellised. We also propose a method for limiting the number of necessary computations during classifier construction. Our method, named one-vs-near, is an extension of typical one-vs-all approach that is used for binary classifiers to work with multiclass problems. We perform experiments of scalability and quality of the implementation. The results show that the proposed solution allows to scale up SVM that gives reasonable quality results. The proposed one-vs-near method significantly improves effectiveness of the classifier construction.
Authors
Additional information
- DOI
- Digital Object Identifier link open in new tab 10.1007/978-3-319-19324-3_60
- Category
- Aktywność konferencyjna
- Type
- materiały konferencyjne indeksowane w Web of Science
- Language
- angielski
- Publication year
- 2015
