Öneriler

Her ne kadar bu tez çalışmasında, sınıflandırma problemleri için geliştirilen yaklaşımlar etkin çözümler sunsa da, transfer öğrenme problemlerinin sadece tümevarımsal olanları için kullanılabilir. Tümdengelimsel ve öğreticisiz transfer öğrenme problemleri için optimizasyon problemleri açısından herhangi bir yaklaşım geliştirilemediği için bir çözüm önerilememiştir. Bu nedenle hedef görev için etiketli veri olmaması durumunda geliştirilen yaklaşımların hiçbiri kullanılamaz. İleride önerilen yöntemler daha da geliştirilerek tümdengelimsel ve öğreticisiz transfer öğrenme problemlerinde de kullanılabilir.

Önerilen yaklaşımlarda, çok fazla sayıda kaynak görev olması durumunda çözüm havuzu çok büyüyebilir ve bu da hesaplama karmaşıklığını arttırabilir. Gelecekte, geliştirilen genetik transfer öğrenme yaklaşımının çözüm havuzu oluşturma metodu, havuzdaki bireyler arası benzerlikler dikkate alınarak ve taranan çözüm uzayını daha etkili biçimde ifade edilecek bireylerin örneklenmesi sağlanarak daha küçük çözüm havuzuyla daha etkili bilgi transferi sağlanmaya çalışılabilir.

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Faks :

e-mail : bariskocer@selcuk.edu.tr

EĞĠTĠM

Derece Adı, Ġlçe, Ġl Bitirme Yılı

Lise : Ünye Anadolu Öğretmen Lisesi, Ünye, Ordu 1999 Üniversite : Selçuk Üniversitesi Bil. Müh., Selçuklu, Konya 2003 Yüksek Lisans : Selçuk Üniversitesi Bil. Müh., Selçuklu, Konya 2006 Doktora : Selçuk Üniversitesi Bil. Müh., Selçuklu, Konya 2012

Ġġ DENEYĠMLERĠ

Yıl Kurum Görevi

2004 - … Selçuk Üniversitesi Bilgisayar Müh. Arş. Gör.

UZMANLIK ALANI

Yapay Zeka, Meta Sezgisel Yöntemler

YABANCI DĠLLER

İngilizce

BELĠRTMEK ĠSTEĞĠNĠZ DĠĞER ÖZELLĠKLER YAYINLAR

Koçer, B., Arslan, A., 2010, Genetic Transfer Learning, Expert System with

Application, 37, 6997 – 7002. (Doktora tezinden türetilmiştir)

Koçer, B., Arslan, A., 2012, Transfer Learning in Vehicle Routing Problem for Rapid Adaptation, International Journal of Innovative Computing, Information and Control, Baskıda. (Doktora tezinden türetilmiştir)