يك روش يادگيري جديد براي افزايش كارايي سيستم طبقه‌بندي مركب
الموضوعات :
سیدحسن نبوی کریزی
1
,
احسان‌اله کبیر
2
1 - دانشگاه تربیت مدرس
2 - دانشگاه تربیت مدرس
الکلمات المفتاحية: طبقه‌بندهايادگيري دسته‌جمعيگوناگوني در خطاهمبستگي منفيشبكه عصبي,
ملخص المقالة :
يادگيري دسته‌جمعي يک رويکرد مؤثر در يادگيري ماشيني است كه به منظور بهبود كارايي سيستم بازشناسي الگو استفاده مي‌شود. براي آنكه اين نوع يادگيري مفيد واقع شود بايد خطاهاي طبقه‌بندهاي پايه با يكديگر متفاوت باشند. راهكارهاي ايجاد تفاوت در خطا، به دو گروه ضمني و صريح تقسيم مي‌شوند. در اين تحقيق روش جديدي از نوع صريح براي ايجاد گوناگوني در طبقه‌بندهاي يك سيستم مركب ارائه مي‌شود. در اين روش، معيار تازه‌اي از گوناگوني در فرايند يادگيري سيستم مركب به كار گرفته مي‌شود. در روش پيشنهادي، شباهت بين خطاي هر طبقه‌بند با طبقه‌بندهاي ديگر به صورت يك مؤلفه در تابع خطاي آن طبقه‌بند منظور شده و در الگوريتم يادگيري آن ايفاي نقش مي‌كند. نتايج آزمايش‌هاي ما بر روي چند مجموعه داده متداول، براي حالتي كه طبقه‌بندهاي پايه از نوع شبكه‌هاي عصبي باشند، نشان مي‌دهند كه روش پيشنهادي ما باعث افزايش كارايي سيستم طبقه‌بندي مركب نسبت به روش‌هاي مشابه آن مي‌شود.
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