کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
407581 | 678158 | 2013 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Sphere Support Vector Machines for large classification tasks
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Sphere Support Vector Machines for large classification tasks Sphere Support Vector Machines for large classification tasks](/preview/png/407581.png)
چکیده انگلیسی
This paper introduces Sphere Support Vector Machines (SVMs) as the new fast classification algorithm based on combining a minimal enclosing ball approach, state of the art nearest point problem solvers and probabilistic techniques. The blending of the three significantly speeds up the training phase of SVMs and also attains practically the same accuracy as the other classification models over several large real datasets within the strict validation frame of a double (nested) cross-validation. The results shown are promoting SphereSVM as outstanding alternatives for handling large and ultra-large datasets in a reasonable time without switching to various parallelization schemes for SVM algorithms recently proposed.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 101, 4 February 2013, Pages 59–67
Journal: Neurocomputing - Volume 101, 4 February 2013, Pages 59–67
نویسندگان
Robert Strack, Vojislav Kecman, Beata Strack, Qi Li,