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  Feature dimensionality reduction for recognition of Persian handwritten letters using a combination of quantum genetic algorithm and neural network در فایل ورد (word) دارای 6 صفحه می باشد و دارای تنظیمات در microsoft word می باشد و آماده پرینت یا چاپ است

فایل ورد Feature dimensionality reduction for recognition of Persian handwritten letters using a combination of quantum genetic algorithm and neural network در فایل ورد (word)  کاملا فرمت بندی و تنظیم شده در استاندارد دانشگاه  و مراکز دولتی می باشد.

توجه : در صورت  مشاهده  بهم ريختگي احتمالي در متون زير ،دليل ان کپي کردن اين مطالب از داخل فایل ورد مي باشد و در فايل اصلي Feature dimensionality reduction for recognition of Persian handwritten letters using a combination of quantum genetic algorithm and neural network در فایل ورد (word)،به هيچ وجه بهم ريختگي وجود ندارد


بخشی از متن Feature dimensionality reduction for recognition of Persian handwritten letters using a combination of quantum genetic algorithm and neural network در فایل ورد (word) :


سال انتشار : 1394

نام کنفرانس یا همایش : هفتمين کنفرانس ملي مهندسي برق و الکترونيک ايران

تعداد صفحات : 6

چکیده مقاله:

Curse of dimensionality is one of the biggest challenges in classification problems. High dimensionality of problem increases classification rate and brings about classification error. Selecting an effective subset of features is an important point in analyzing correlation rate in classification issues. The main purpose of this paper is enhancing characters recognition and classification, creating quick and low-cost classes, and eventually recognizing Persian handwritten characters more accurately and faster. In this paper, to reduce feature dimensionality of datasets a hybrid approach using artificial neural network, genetic algorithm and quantum genetic algorithm is proposed that can be used to distinguish Persian handwritten letters. Implementation results show that proposed algorithms are able to reduce number of features by 19% to 49%. They also show that recognition and classification accuracy of resulted subset of features has risen, by 7/31%, comparing to primitive dataset.

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