کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
406117 678064 2016 14 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Comparison of combining methods using Extreme Learning Machines under small sample scenario
ترجمه فارسی عنوان
مقایسه روش های ترکیب با استفاده از ماشین های آموزش افراطی تحت سناریو نمونه کوچک
کلمات کلیدی
ماشین آموزش عالی داده های کوچک نمونه، انتخاب مدل، ترکیبی از مدل، متوسط ​​تقارن مدل مالو، مدل متوسط ​​جک نایف
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Making accurate predictions is a difficult task that is encountered throughout many research domains. In certain cases, the number of available samples is so scarce that providing reliable estimates is a challenging problem. In this paper, we are interested in giving as accurate predictions as possible based on the Extreme Learning Machine type of a neural network in small sample data scenarios. Most of the Extreme Learning Machine literature is focused on choosing a particular model from a pool of candidates, but such approach usually ignores model selection uncertainty and has inferior performance compared to combining methods. We empirically examine several model selection criteria coupled with new model combining approaches that were recently proposed. The results obtained indicate that a careful choice among the combinations must be performed in order to have the most accurate and stable predictions.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 174, Part A, 22 January 2016, Pages 4–17
نویسندگان
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