A New Hybrid Expansion Function Based Mutual Information for a Multilayer Neural Networks Optimization
| المصدر: | مجلة المالية وحوكمة الشركات |
|---|---|
| الناشر: | جامعة العربي بن مهيدي أم البواقي - كلية العلوم الاقتصادية والعلوم التجارية وعلوم التسيير و مخبر الابتكار والهندسة المالية |
| المؤلف الرئيسي: | Ncibi, Kais (Author) |
| مؤلفين آخرين: | Djenina, Amor (Co-Author) , Sadraoui, Tarek (Co-Author) , Mili, Faycel (Co-Author) |
| المجلد/العدد: | مج1, ع2 |
| محكمة: | نعم |
| الدولة: |
الجزائر |
| التاريخ الميلادي: |
2017
|
| الشهر: | ديسمبر |
| الصفحات: | 7 - 23 |
| ISSN: |
2602-5655 |
| رقم MD: | 1041688 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Function Expansion | Multilayer Perceptron | Classification | Mutual Information | Features Selection
|
| رابط المحتوى: |
عدد مرات التحميل
2
| المستخلص: |
Function expansion was used to expand initial features based on a non linear transformation. Many known expansion functions are found such the trigonometric, the polynomial, the Legendre polynomial, the power series, the exponential and the logarithmic transformation. This paper present a comparison between different expansion functions based on mutual information and different performance functions. We propose a new expansion process able to improve the correspondent mutual information and the final performance. The process was tested; using different benchmark databases, and shows his ability to improve results of classification problems. |
|---|---|
| ISSN: |
2602-5655 |
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