Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network
المؤلفون المشاركون
Manjula Devi, R.
Kuppuswami, S.
Suganthe, R. C.
المصدر
Mathematical Problems in Engineering
العدد
المجلد 2013، العدد 2013 (31 ديسمبر/كانون الأول 2013)، ص ص. 1-9، 9ص.
الناشر
Hindawi Publishing Corporation
تاريخ النشر
2013-06-25
دولة النشر
مصر
عدد الصفحات
9
التخصصات الرئيسية
الملخص EN
Artificial neural network has been extensively consumed training model for solving pattern recognition tasks.
However, training a very huge training data set using complex neural network necessitates excessively high training time.
In this correspondence, a new fast Linear Adaptive Skipping Training (LAST) algorithm for training artificial neural network (ANN) is instituted.
The core essence of this paper is to ameliorate the training speed of ANN by exhibiting only the input samples that do not categorize perfectly in the previous epoch which dynamically reducing the number of input samples exhibited to the network at every single epoch without affecting the network’s accuracy.
Thus decreasing the size of the training set can reduce the training time, thereby ameliorating the training speed.
This LAST algorithm also determines how many epochs the particular input sample has to skip depending upon the successful classification of that input sample.
This LAST algorithm can be incorporated into any supervised training algorithms.
Experimental result shows that the training speed attained by LAST algorithm is preferably higher than that of other conventional training algorithms.
نمط استشهاد جمعية علماء النفس الأمريكية (APA)
Manjula Devi, R.& Kuppuswami, S.& Suganthe, R. C.. 2013. Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1009086
نمط استشهاد الجمعية الأمريكية للغات الحديثة (MLA)
Manjula Devi, R.…[et al.]. Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network. Mathematical Problems in Engineering No. 2013 (2013), pp.1-9.
https://search.emarefa.net/detail/BIM-1009086
نمط استشهاد الجمعية الطبية الأمريكية (AMA)
Manjula Devi, R.& Kuppuswami, S.& Suganthe, R. C.. Fast Linear Adaptive Skipping Training Algorithm for Training Artificial Neural Network. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-9.
https://search.emarefa.net/detail/BIM-1009086
نوع البيانات
مقالات
لغة النص
الإنجليزية
الملاحظات
Includes bibliographical references
رقم السجل
BIM-1009086
قاعدة معامل التأثير والاستشهادات المرجعية العربي "ارسيف Arcif"
أضخم قاعدة بيانات عربية للاستشهادات المرجعية للمجلات العلمية المحكمة الصادرة في العالم العربي
تقوم هذه الخدمة بالتحقق من التشابه أو الانتحال في الأبحاث والمقالات العلمية والأطروحات الجامعية والكتب والأبحاث باللغة العربية، وتحديد درجة التشابه أو أصالة الأعمال البحثية وحماية ملكيتها الفكرية. تعرف اكثر