کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
410343 | 679137 | 2013 | 9 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Machine learning using Bernoulli mixture models: Clustering, rule extraction and dimensionality reduction
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
Probabilistic models are common in the machine learning community for representing and modeling data. In this paper we focus on a probabilistic model based upon Bernoulli mixture models to solve different types of problems in pattern recognition like feature selection, classification, dimensionality reduction and rule generation. We illustrate the effectiveness of Bernoulli mixture models by applying them to various real life datasets taken from different domains, and used as part of various machine learning challenges. Our algorithms, based upon Bernoulli mixture models, are not only simple and intuitive but have also proven to give accurate and good results.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 119, 7 November 2013, Pages 366–374
Journal: Neurocomputing - Volume 119, 7 November 2013, Pages 366–374
نویسندگان
Mehreen Saeed, Kashif Javed, Haroon Atique Babri,