کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
1135779 956114 2011 7 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Pattern recognition using boundary data of component distributions
موضوعات مرتبط
مهندسی و علوم پایه سایر رشته های مهندسی مهندسی صنعتی و تولید
پیش نمایش صفحه اول مقاله
Pattern recognition using boundary data of component distributions
چکیده انگلیسی

In statistical pattern recognition, a Gaussian mixture model is sometimes used for representing the distribution of vectors. The parameters of the Gaussian mixture model are usually estimated from given sample data by the expectation maximization algorithm. However, when the number of data attributes is large, the parameters cannot be estimated correctly. In this paper, we propose a novel approach for estimating the parameters of the Gaussian mixture model by using sample data located on the boundary of regions defined by the component density functions. Experiments are carried out to show the characteristics of the proposed method.

Research highlights
► Novel approach for estimating Gaussian mixture models is proposed.
► Data on the boundary of regions defined by the component density functions are used.
► The proposed method is effective especially when the number of samples is small.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Computers & Industrial Engineering - Volume 60, Issue 3, April 2011, Pages 466–472
نویسندگان
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