کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
531308 | 869827 | 2009 | 7 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Learning mixture models with support vector machines for sequence classification and segmentation
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
This paper focuses on learning recognition systems able to cope with sequential data for classification and segmentation tasks. It investigates the integration of discriminant power in the learning of generative models, which are usually used for such data. Based on a procedure that transforms a sample data into a generative model, learning is viewed as the selection of efficient component models in a mixture of generative models. This may be done through the learning of a support vector machine. We propose a few kernels for this and report experimental results for classification and segmentation tasks.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 42, Issue 12, December 2009, Pages 3224–3230
Journal: Pattern Recognition - Volume 42, Issue 12, December 2009, Pages 3224–3230
نویسندگان
Trinh Minh Tri Do, Thierry Artières,